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Former-commit-id: 268fb7e6825ddfc1165fa7adc7c216f9d61005da [formerly 06376993a831c060c337ec6e7540252f0b2dfe09]
Former-commit-id: c4812d40187a76a878e7d215d22ee84811b41896

fawkes/Untitled.ipynb.REMOVED.git-id

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+58d500da850206b845bdd0150fa182a0ff8c50f0

fawkes/align_face.py

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+import detect_face
+import numpy as np
+import tensorflow as tf
+
+# modify the default parameters of np.load
+np_load_old = np.load
+np.load = lambda *a, **k: np_load_old(*a, allow_pickle=True, **k)
+
+
+def to_rgb(img):
+    w, h = img.shape
+    ret = np.empty((w, h, 3), dtype=np.uint8)
+    ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
+    return ret
+
+
+def aligner(sess):
+    pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
+    return [pnet, rnet, onet]
+
+
+def align(orig_img, aligner, margin=0.8, detect_multiple_faces=True):
+    pnet, rnet, onet = aligner
+    minsize = 20  # minimum size of face
+    threshold = [0.6, 0.7, 0.7]  # three steps's threshold
+    factor = 0.709  # scale factor
+
+    if orig_img.ndim < 2:
+        return None
+    if orig_img.ndim == 2:
+        orig_img = to_rgb(orig_img)
+    orig_img = orig_img[:, :, 0:3]
+
+    bounding_boxes, _ = detect_face.detect_face(orig_img, minsize, pnet, rnet, onet, threshold, factor)
+    nrof_faces = bounding_boxes.shape[0]
+    if nrof_faces > 0:
+        det = bounding_boxes[:, 0:4]
+        det_arr = []
+        img_size = np.asarray(orig_img.shape)[0:2]
+        if nrof_faces > 1:
+            margin = margin / 1.5
+            if detect_multiple_faces:
+                for i in range(nrof_faces):
+                    det_arr.append(np.squeeze(det[i]))
+            else:
+                bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
+                img_center = img_size / 2
+                offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1],
+                                     (det[:, 1] + det[:, 3]) / 2 - img_center[0]])
+                offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
+                index = np.argmax(bounding_box_size - offset_dist_squared * 2.0)  # some extra weight on the centering
+                det_arr.append(det[index, :])
+        else:
+            det_arr.append(np.squeeze(det))
+        cropped_arr = []
+        bounding_boxes_arr = []
+        for i, det in enumerate(det_arr):
+            det = np.squeeze(det)
+            bb = np.zeros(4, dtype=np.int32)
+            side_1 = int((det[2] - det[0]) * margin)
+            side_2 = int((det[3] - det[1]) * margin)
+
+            bb[0] = np.maximum(det[0] - side_1 / 2, 0)
+            bb[1] = np.maximum(det[1] - side_1 / 2, 0)
+            bb[2] = np.minimum(det[2] + side_2 / 2, img_size[1])
+            bb[3] = np.minimum(det[3] + side_2 / 2, img_size[0])
+            cropped = orig_img[bb[1]:bb[3], bb[0]:bb[2], :]
+            cropped_arr.append(cropped)
+            bounding_boxes_arr.append([bb[0], bb[1], bb[2], bb[3]])
+            # scaled = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
+        return cropped_arr, bounding_boxes_arr
+    else:
+        return None
+#
+# if __name__ == '__main__':
+#     orig_img = misc.imread('orig_img.jpeg')
+#     cropped_arr, bounding_boxes_arr = align(orig_img)
+#     misc.imsave('test_output.jpeg', cropped_arr[0])
+#     print(bounding_boxes_arr)
+#

fawkes/detect_face.py

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+""" Tensorflow implementation of the face detection / alignment algorithm found at
+https://github.com/kpzhang93/MTCNN_face_detection_alignment
+"""
+# MIT License
+# 
+# Copyright (c) 2016 David Sandberg
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+# 
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# 
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+# from math import floor
+import cv2
+import numpy as np
+import tensorflow as tf
+from six import string_types, iteritems
+
+
+def layer(op):
+    """Decorator for composable network layers."""
+
+    def layer_decorated(self, *args, **kwargs):
+        # Automatically set a name if not provided.
+        name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
+        # Figure out the layer inputs.
+        if len(self.terminals) == 0:
+            raise RuntimeError('No input variables found for layer %s.' % name)
+        elif len(self.terminals) == 1:
+            layer_input = self.terminals[0]
+        else:
+            layer_input = list(self.terminals)
+        # Perform the operation and get the output.
+        layer_output = op(self, layer_input, *args, **kwargs)
+        # Add to layer LUT.
+        self.layers[name] = layer_output
+        # This output is now the input for the next layer.
+        self.feed(layer_output)
+        # Return self for chained calls.
+        return self
+
+    return layer_decorated
+
+
+class Network(object):
+
+    def __init__(self, inputs, trainable=True):
+        # The input nodes for this network
+        self.inputs = inputs
+        # The current list of terminal nodes
+        self.terminals = []
+        # Mapping from layer names to layers
+        self.layers = dict(inputs)
+        # If true, the resulting variables are set as trainable
+        self.trainable = trainable
+
+        self.setup()
+
+    def setup(self):
+        """Construct the network. """
+        raise NotImplementedError('Must be implemented by the subclass.')
+
+    def load(self, data_path, session, ignore_missing=False):
+        """Load network weights.
+        data_path: The path to the numpy-serialized network weights
+        session: The current TensorFlow session
+        ignore_missing: If true, serialized weights for missing layers are ignored.
+        """
+        data_dict = np.load(data_path, encoding='latin1').item()  # pylint: disable=no-member
+
+        for op_name in data_dict:
+            with tf.variable_scope(op_name, reuse=True):
+                for param_name, data in iteritems(data_dict[op_name]):
+                    try:
+                        var = tf.get_variable(param_name)
+                        session.run(var.assign(data))
+                    except ValueError:
+                        if not ignore_missing:
+                            raise
+
+    def feed(self, *args):
+        """Set the input(s) for the next operation by replacing the terminal nodes.
+        The arguments can be either layer names or the actual layers.
+        """
+        assert len(args) != 0
+        self.terminals = []
+        for fed_layer in args:
+            if isinstance(fed_layer, string_types):
+                try:
+                    fed_layer = self.layers[fed_layer]
+                except KeyError:
+                    raise KeyError('Unknown layer name fed: %s' % fed_layer)
+            self.terminals.append(fed_layer)
+        return self
+
+    def get_output(self):
+        """Returns the current network output."""
+        return self.terminals[-1]
+
+    def get_unique_name(self, prefix):
+        """Returns an index-suffixed unique name for the given prefix.
+        This is used for auto-generating layer names based on the type-prefix.
+        """
+        ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
+        return '%s_%d' % (prefix, ident)
+
+    def make_var(self, name, shape):
+        """Creates a new TensorFlow variable."""
+        return tf.get_variable(name, shape, trainable=self.trainable)
+
+    def validate_padding(self, padding):
+        """Verifies that the padding is one of the supported ones."""
+        assert padding in ('SAME', 'VALID')
+
+    @layer
+    def conv(self,
+             inp,
+             k_h,
+             k_w,
+             c_o,
+             s_h,
+             s_w,
+             name,
+             relu=True,
+             padding='SAME',
+             group=1,
+             biased=True):
+        # Verify that the padding is acceptable
+        self.validate_padding(padding)
+        # Get the number of channels in the input
+        c_i = int(inp.get_shape()[-1])
+        # Verify that the grouping parameter is valid
+        assert c_i % group == 0
+        assert c_o % group == 0
+        # Convolution for a given input and kernel
+        convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
+        with tf.variable_scope(name) as scope:
+            kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
+            # This is the common-case. Convolve the input without any further complications.
+            output = convolve(inp, kernel)
+            # Add the biases
+            if biased:
+                biases = self.make_var('biases', [c_o])
+                output = tf.nn.bias_add(output, biases)
+            if relu:
+                # ReLU non-linearity
+                output = tf.nn.relu(output, name=scope.name)
+            return output
+
+    @layer
+    def prelu(self, inp, name):
+        with tf.variable_scope(name):
+            i = int(inp.get_shape()[-1])
+            alpha = self.make_var('alpha', shape=(i,))
+            output = tf.nn.relu(inp) + tf.multiply(alpha, -tf.nn.relu(-inp))
+        return output
+
+    @layer
+    def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
+        self.validate_padding(padding)
+        return tf.nn.max_pool(inp,
+                              ksize=[1, k_h, k_w, 1],
+                              strides=[1, s_h, s_w, 1],
+                              padding=padding,
+                              name=name)
+
+    @layer
+    def fc(self, inp, num_out, name, relu=True):
+        with tf.variable_scope(name):
+            input_shape = inp.get_shape()
+            if input_shape.ndims == 4:
+                # The input is spatial. Vectorize it first.
+                dim = 1
+                for d in input_shape[1:].as_list():
+                    dim *= int(d)
+                feed_in = tf.reshape(inp, [-1, dim])
+            else:
+                feed_in, dim = (inp, input_shape[-1].value)
+            weights = self.make_var('weights', shape=[dim, num_out])
+            biases = self.make_var('biases', [num_out])
+            op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
+            fc = op(feed_in, weights, biases, name=name)
+            return fc
+
+    """
+    Multi dimensional softmax,
+    refer to https://github.com/tensorflow/tensorflow/issues/210
+    compute softmax along the dimension of target
+    the native softmax only supports batch_size x dimension
+    """
+
+    @layer
+    def softmax(self, target, axis, name=None):
+        max_axis = tf.reduce_max(target, axis, keepdims=True)
+        target_exp = tf.exp(target - max_axis)
+        normalize = tf.reduce_sum(target_exp, axis, keepdims=True)
+        softmax = tf.div(target_exp, normalize, name)
+        return softmax
+
+
+class PNet(Network):
+    def setup(self):
+        (self.feed('data')  # pylint: disable=no-value-for-parameter, no-member
+         .conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')
+         .prelu(name='PReLU1')
+         .max_pool(2, 2, 2, 2, name='pool1')
+         .conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2')
+         .prelu(name='PReLU2')
+         .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3')
+         .prelu(name='PReLU3')
+         .conv(1, 1, 2, 1, 1, relu=False, name='conv4-1')
+         .softmax(3, name='prob1'))
+
+        (self.feed('PReLU3')  # pylint: disable=no-value-for-parameter
+         .conv(1, 1, 4, 1, 1, relu=False, name='conv4-2'))
+
+
+class RNet(Network):
+    def setup(self):
+        (self.feed('data')  # pylint: disable=no-value-for-parameter, no-member
+         .conv(3, 3, 28, 1, 1, padding='VALID', relu=False, name='conv1')
+         .prelu(name='prelu1')
+         .max_pool(3, 3, 2, 2, name='pool1')
+         .conv(3, 3, 48, 1, 1, padding='VALID', relu=False, name='conv2')
+         .prelu(name='prelu2')
+         .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
+         .conv(2, 2, 64, 1, 1, padding='VALID', relu=False, name='conv3')
+         .prelu(name='prelu3')
+         .fc(128, relu=False, name='conv4')
+         .prelu(name='prelu4')
+         .fc(2, relu=False, name='conv5-1')
+         .softmax(1, name='prob1'))
+
+        (self.feed('prelu4')  # pylint: disable=no-value-for-parameter
+         .fc(4, relu=False, name='conv5-2'))
+
+
+class ONet(Network):
+    def setup(self):
+        (self.feed('data')  # pylint: disable=no-value-for-parameter, no-member
+         .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1')
+         .prelu(name='prelu1')
+         .max_pool(3, 3, 2, 2, name='pool1')
+         .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2')
+         .prelu(name='prelu2')
+         .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
+         .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3')
+         .prelu(name='prelu3')
+         .max_pool(2, 2, 2, 2, name='pool3')
+         .conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4')
+         .prelu(name='prelu4')
+         .fc(256, relu=False, name='conv5')
+         .prelu(name='prelu5')
+         .fc(2, relu=False, name='conv6-1')
+         .softmax(1, name='prob1'))
+
+        (self.feed('prelu5')  # pylint: disable=no-value-for-parameter
+         .fc(4, relu=False, name='conv6-2'))
+
+        (self.feed('prelu5')  # pylint: disable=no-value-for-parameter
+         .fc(10, relu=False, name='conv6-3'))
+
+
+def create_mtcnn(sess, model_path):
+    if not model_path:
+        model_path, _ = os.path.split(os.path.realpath(__file__))
+
+    with tf.variable_scope('pnet'):
+        data = tf.placeholder(tf.float32, (None, None, None, 3), 'input')
+        pnet = PNet({'data': data})
+        pnet.load(os.path.join(model_path, 'weights/det1.npy'), sess)
+    with tf.variable_scope('rnet'):
+        data = tf.placeholder(tf.float32, (None, 24, 24, 3), 'input')
+        rnet = RNet({'data': data})
+        rnet.load(os.path.join(model_path, 'weights/det2.npy'), sess)
+    with tf.variable_scope('onet'):
+        data = tf.placeholder(tf.float32, (None, 48, 48, 3), 'input')
+        onet = ONet({'data': data})
+        onet.load(os.path.join(model_path, 'weights/det3.npy'), sess)
+
+    pnet_fun = lambda img: sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0': img})
+    rnet_fun = lambda img: sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0': img})
+    onet_fun = lambda img: sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'),
+                                    feed_dict={'onet/input:0': img})
+    return pnet_fun, rnet_fun, onet_fun
+
+
+def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
+    """Detects faces in an image, and returns bounding boxes and points for them.
+    img: input image
+    minsize: minimum faces' size
+    pnet, rnet, onet: caffemodel
+    threshold: threshold=[th1, th2, th3], th1-3 are three steps's threshold
+    factor: the factor used to create a scaling pyramid of face sizes to detect in the image.
+    """
+    factor_count = 0
+    total_boxes = np.empty((0, 9))
+    points = np.empty(0)
+    h = img.shape[0]
+    w = img.shape[1]
+    minl = np.amin([h, w])
+    m = 12.0 / minsize
+    minl = minl * m
+    # create scale pyramid
+    scales = []
+    while minl >= 12:
+        scales += [m * np.power(factor, factor_count)]
+        minl = minl * factor
+        factor_count += 1
+
+    # first stage
+    for scale in scales:
+        hs = int(np.ceil(h * scale))
+        ws = int(np.ceil(w * scale))
+        im_data = imresample(img, (hs, ws))
+        im_data = (im_data - 127.5) * 0.0078125
+        img_x = np.expand_dims(im_data, 0)
+        img_y = np.transpose(img_x, (0, 2, 1, 3))
+        out = pnet(img_y)
+        out0 = np.transpose(out[0], (0, 2, 1, 3))
+        out1 = np.transpose(out[1], (0, 2, 1, 3))
+
+        boxes, _ = generateBoundingBox(out1[0, :, :, 1].copy(), out0[0, :, :, :].copy(), scale, threshold[0])
+
+        # inter-scale nms
+        pick = nms(boxes.copy(), 0.5, 'Union')
+        if boxes.size > 0 and pick.size > 0:
+            boxes = boxes[pick, :]
+            total_boxes = np.append(total_boxes, boxes, axis=0)
+
+    numbox = total_boxes.shape[0]
+    if numbox > 0:
+        pick = nms(total_boxes.copy(), 0.7, 'Union')
+        total_boxes = total_boxes[pick, :]
+        regw = total_boxes[:, 2] - total_boxes[:, 0]
+        regh = total_boxes[:, 3] - total_boxes[:, 1]
+        qq1 = total_boxes[:, 0] + total_boxes[:, 5] * regw
+        qq2 = total_boxes[:, 1] + total_boxes[:, 6] * regh
+        qq3 = total_boxes[:, 2] + total_boxes[:, 7] * regw
+        qq4 = total_boxes[:, 3] + total_boxes[:, 8] * regh
+        total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:, 4]]))
+        total_boxes = rerec(total_boxes.copy())
+        total_boxes[:, 0:4] = np.fix(total_boxes[:, 0:4]).astype(np.int32)
+        dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
+
+    numbox = total_boxes.shape[0]
+    if numbox > 0:
+        # second stage
+        tempimg = np.zeros((24, 24, 3, numbox))
+        for k in range(0, numbox):
+            tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
+            tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = img[y[k] - 1:ey[k], x[k] - 1:ex[k], :]
+            if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
+                tempimg[:, :, :, k] = imresample(tmp, (24, 24))
+            else:
+                return np.empty()
+        tempimg = (tempimg - 127.5) * 0.0078125
+        tempimg1 = np.transpose(tempimg, (3, 1, 0, 2))
+        out = rnet(tempimg1)
+        out0 = np.transpose(out[0])
+        out1 = np.transpose(out[1])
+        score = out1[1, :]
+        ipass = np.where(score > threshold[1])
+        total_boxes = np.hstack([total_boxes[ipass[0], 0:4].copy(), np.expand_dims(score[ipass].copy(), 1)])
+        mv = out0[:, ipass[0]]
+        if total_boxes.shape[0] > 0:
+            pick = nms(total_boxes, 0.7, 'Union')
+            total_boxes = total_boxes[pick, :]
+            total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:, pick]))
+            total_boxes = rerec(total_boxes.copy())
+
+    numbox = total_boxes.shape[0]
+    if numbox > 0:
+        # third stage
+        total_boxes = np.fix(total_boxes).astype(np.int32)
+        dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
+        tempimg = np.zeros((48, 48, 3, numbox))
+        for k in range(0, numbox):
+            tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
+            tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = img[y[k] - 1:ey[k], x[k] - 1:ex[k], :]
+            if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
+                tempimg[:, :, :, k] = imresample(tmp, (48, 48))
+            else:
+                return np.empty()
+        tempimg = (tempimg - 127.5) * 0.0078125
+        tempimg1 = np.transpose(tempimg, (3, 1, 0, 2))
+        out = onet(tempimg1)
+        out0 = np.transpose(out[0])
+        out1 = np.transpose(out[1])
+        out2 = np.transpose(out[2])
+        score = out2[1, :]
+        points = out1
+        ipass = np.where(score > threshold[2])
+        points = points[:, ipass[0]]
+        total_boxes = np.hstack([total_boxes[ipass[0], 0:4].copy(), np.expand_dims(score[ipass].copy(), 1)])
+        mv = out0[:, ipass[0]]
+
+        w = total_boxes[:, 2] - total_boxes[:, 0] + 1
+        h = total_boxes[:, 3] - total_boxes[:, 1] + 1
+        points[0:5, :] = np.tile(w, (5, 1)) * points[0:5, :] + np.tile(total_boxes[:, 0], (5, 1)) - 1
+        points[5:10, :] = np.tile(h, (5, 1)) * points[5:10, :] + np.tile(total_boxes[:, 1], (5, 1)) - 1
+        if total_boxes.shape[0] > 0:
+            total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))
+            pick = nms(total_boxes.copy(), 0.7, 'Min')
+            total_boxes = total_boxes[pick, :]
+            points = points[:, pick]
+
+    return total_boxes, points
+
+
+def bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, onet, threshold, factor):
+    """Detects faces in a list of images
+    images: list containing input images
+    detection_window_size_ratio: ratio of minimum face size to smallest image dimension
+    pnet, rnet, onet: caffemodel
+    threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold [0-1]
+    factor: the factor used to create a scaling pyramid of face sizes to detect in the image.
+    """
+    all_scales = [None] * len(images)
+    images_with_boxes = [None] * len(images)
+
+    for i in range(len(images)):
+        images_with_boxes[i] = {'total_boxes': np.empty((0, 9))}
+
+    # create scale pyramid
+    for index, img in enumerate(images):
+        all_scales[index] = []
+        h = img.shape[0]
+        w = img.shape[1]
+        minsize = int(detection_window_size_ratio * np.minimum(w, h))
+        factor_count = 0
+        minl = np.amin([h, w])
+        if minsize <= 12:
+            minsize = 12
+
+        m = 12.0 / minsize
+        minl = minl * m
+        while minl >= 12:
+            all_scales[index].append(m * np.power(factor, factor_count))
+            minl = minl * factor
+            factor_count += 1
+
+    # # # # # # # # # # # # #
+    # first stage - fast proposal network (pnet) to obtain face candidates
+    # # # # # # # # # # # # #
+
+    images_obj_per_resolution = {}
+
+    # TODO: use some type of rounding to number module 8 to increase probability that pyramid images will have the same resolution across input images
+
+    for index, scales in enumerate(all_scales):
+        h = images[index].shape[0]
+        w = images[index].shape[1]
+
+        for scale in scales:
+            hs = int(np.ceil(h * scale))
+            ws = int(np.ceil(w * scale))
+
+            if (ws, hs) not in images_obj_per_resolution:
+                images_obj_per_resolution[(ws, hs)] = []
+
+            im_data = imresample(images[index], (hs, ws))
+            im_data = (im_data - 127.5) * 0.0078125
+            img_y = np.transpose(im_data, (1, 0, 2))  # caffe uses different dimensions ordering
+            images_obj_per_resolution[(ws, hs)].append({'scale': scale, 'image': img_y, 'index': index})
+
+    for resolution in images_obj_per_resolution:
+        images_per_resolution = [i['image'] for i in images_obj_per_resolution[resolution]]
+        outs = pnet(images_per_resolution)
+
+        for index in range(len(outs[0])):
+            scale = images_obj_per_resolution[resolution][index]['scale']
+            image_index = images_obj_per_resolution[resolution][index]['index']
+            out0 = np.transpose(outs[0][index], (1, 0, 2))
+            out1 = np.transpose(outs[1][index], (1, 0, 2))
+
+            boxes, _ = generateBoundingBox(out1[:, :, 1].copy(), out0[:, :, :].copy(), scale, threshold[0])
+
+            # inter-scale nms
+            pick = nms(boxes.copy(), 0.5, 'Union')
+            if boxes.size > 0 and pick.size > 0:
+                boxes = boxes[pick, :]
+                images_with_boxes[image_index]['total_boxes'] = np.append(images_with_boxes[image_index]['total_boxes'],
+                                                                          boxes,
+                                                                          axis=0)
+
+    for index, image_obj in enumerate(images_with_boxes):
+        numbox = image_obj['total_boxes'].shape[0]
+        if numbox > 0:
+            h = images[index].shape[0]
+            w = images[index].shape[1]
+            pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Union')
+            image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
+            regw = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0]
+            regh = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1]
+            qq1 = image_obj['total_boxes'][:, 0] + image_obj['total_boxes'][:, 5] * regw
+            qq2 = image_obj['total_boxes'][:, 1] + image_obj['total_boxes'][:, 6] * regh
+            qq3 = image_obj['total_boxes'][:, 2] + image_obj['total_boxes'][:, 7] * regw
+            qq4 = image_obj['total_boxes'][:, 3] + image_obj['total_boxes'][:, 8] * regh
+            image_obj['total_boxes'] = np.transpose(np.vstack([qq1, qq2, qq3, qq4, image_obj['total_boxes'][:, 4]]))
+            image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
+            image_obj['total_boxes'][:, 0:4] = np.fix(image_obj['total_boxes'][:, 0:4]).astype(np.int32)
+            dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
+
+            numbox = image_obj['total_boxes'].shape[0]
+            tempimg = np.zeros((24, 24, 3, numbox))
+
+            if numbox > 0:
+                for k in range(0, numbox):
+                    tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
+                    tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
+                    if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
+                        tempimg[:, :, :, k] = imresample(tmp, (24, 24))
+                    else:
+                        return np.empty()
+
+                tempimg = (tempimg - 127.5) * 0.0078125
+                image_obj['rnet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
+
+    # # # # # # # # # # # # #
+    # second stage - refinement of face candidates with rnet
+    # # # # # # # # # # # # #
+
+    bulk_rnet_input = np.empty((0, 24, 24, 3))
+    for index, image_obj in enumerate(images_with_boxes):
+        if 'rnet_input' in image_obj:
+            bulk_rnet_input = np.append(bulk_rnet_input, image_obj['rnet_input'], axis=0)
+
+    out = rnet(bulk_rnet_input)
+    out0 = np.transpose(out[0])
+    out1 = np.transpose(out[1])
+    score = out1[1, :]
+
+    i = 0
+    for index, image_obj in enumerate(images_with_boxes):
+        if 'rnet_input' not in image_obj:
+            continue
+
+        rnet_input_count = image_obj['rnet_input'].shape[0]
+        score_per_image = score[i:i + rnet_input_count]
+        out0_per_image = out0[:, i:i + rnet_input_count]
+
+        ipass = np.where(score_per_image > threshold[1])
+        image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
+                                              np.expand_dims(score_per_image[ipass].copy(), 1)])
+
+        mv = out0_per_image[:, ipass[0]]
+
+        if image_obj['total_boxes'].shape[0] > 0:
+            h = images[index].shape[0]
+            w = images[index].shape[1]
+            pick = nms(image_obj['total_boxes'], 0.7, 'Union')
+            image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
+            image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv[:, pick]))
+            image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
+
+            numbox = image_obj['total_boxes'].shape[0]
+
+            if numbox > 0:
+                tempimg = np.zeros((48, 48, 3, numbox))
+                image_obj['total_boxes'] = np.fix(image_obj['total_boxes']).astype(np.int32)
+                dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
+
+                for k in range(0, numbox):
+                    tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
+                    tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
+                    if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
+                        tempimg[:, :, :, k] = imresample(tmp, (48, 48))
+                    else:
+                        return np.empty()
+                tempimg = (tempimg - 127.5) * 0.0078125
+                image_obj['onet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
+
+        i += rnet_input_count
+
+    # # # # # # # # # # # # #
+    # third stage - further refinement and facial landmarks positions with onet
+    # # # # # # # # # # # # #
+
+    bulk_onet_input = np.empty((0, 48, 48, 3))
+    for index, image_obj in enumerate(images_with_boxes):
+        if 'onet_input' in image_obj:
+            bulk_onet_input = np.append(bulk_onet_input, image_obj['onet_input'], axis=0)
+
+    out = onet(bulk_onet_input)
+
+    out0 = np.transpose(out[0])
+    out1 = np.transpose(out[1])
+    out2 = np.transpose(out[2])
+    score = out2[1, :]
+    points = out1
+
+    i = 0
+    ret = []
+    for index, image_obj in enumerate(images_with_boxes):
+        if 'onet_input' not in image_obj:
+            ret.append(None)
+            continue
+
+        onet_input_count = image_obj['onet_input'].shape[0]
+
+        out0_per_image = out0[:, i:i + onet_input_count]
+        score_per_image = score[i:i + onet_input_count]
+        points_per_image = points[:, i:i + onet_input_count]
+
+        ipass = np.where(score_per_image > threshold[2])
+        points_per_image = points_per_image[:, ipass[0]]
+
+        image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
+                                              np.expand_dims(score_per_image[ipass].copy(), 1)])
+        mv = out0_per_image[:, ipass[0]]
+
+        w = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0] + 1
+        h = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1] + 1
+        points_per_image[0:5, :] = np.tile(w, (5, 1)) * points_per_image[0:5, :] + np.tile(
+            image_obj['total_boxes'][:, 0], (5, 1)) - 1
+        points_per_image[5:10, :] = np.tile(h, (5, 1)) * points_per_image[5:10, :] + np.tile(
+            image_obj['total_boxes'][:, 1], (5, 1)) - 1
+
+        if image_obj['total_boxes'].shape[0] > 0:
+            image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv))
+            pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Min')
+            image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
+            points_per_image = points_per_image[:, pick]
+
+            ret.append((image_obj['total_boxes'], points_per_image))
+        else:
+            ret.append(None)
+
+        i += onet_input_count
+
+    return ret
+
+
+# function [boundingbox] = bbreg(boundingbox,reg)
+def bbreg(boundingbox, reg):
+    """Calibrate bounding boxes"""
+    if reg.shape[1] == 1:
+        reg = np.reshape(reg, (reg.shape[2], reg.shape[3]))
+
+    w = boundingbox[:, 2] - boundingbox[:, 0] + 1
+    h = boundingbox[:, 3] - boundingbox[:, 1] + 1
+    b1 = boundingbox[:, 0] + reg[:, 0] * w
+    b2 = boundingbox[:, 1] + reg[:, 1] * h
+    b3 = boundingbox[:, 2] + reg[:, 2] * w
+    b4 = boundingbox[:, 3] + reg[:, 3] * h
+    boundingbox[:, 0:4] = np.transpose(np.vstack([b1, b2, b3, b4]))
+    return boundingbox
+
+
+def generateBoundingBox(imap, reg, scale, t):
+    """Use heatmap to generate bounding boxes"""
+    stride = 2
+    cellsize = 12
+
+    imap = np.transpose(imap)
+    dx1 = np.transpose(reg[:, :, 0])
+    dy1 = np.transpose(reg[:, :, 1])
+    dx2 = np.transpose(reg[:, :, 2])
+    dy2 = np.transpose(reg[:, :, 3])
+    y, x = np.where(imap >= t)
+    if y.shape[0] == 1:
+        dx1 = np.flipud(dx1)
+        dy1 = np.flipud(dy1)
+        dx2 = np.flipud(dx2)
+        dy2 = np.flipud(dy2)
+    score = imap[(y, x)]
+    reg = np.transpose(np.vstack([dx1[(y, x)], dy1[(y, x)], dx2[(y, x)], dy2[(y, x)]]))
+    if reg.size == 0:
+        reg = np.empty((0, 3))
+    bb = np.transpose(np.vstack([y, x]))
+    q1 = np.fix((stride * bb + 1) / scale)
+    q2 = np.fix((stride * bb + cellsize - 1 + 1) / scale)
+    boundingbox = np.hstack([q1, q2, np.expand_dims(score, 1), reg])
+    return boundingbox, reg
+
+
+# function pick = nms(boxes,threshold,type)
+def nms(boxes, threshold, method):
+    if boxes.size == 0:
+        return np.empty((0, 3))
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    s = boxes[:, 4]
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    I = np.argsort(s)
+    pick = np.zeros_like(s, dtype=np.int16)
+    counter = 0
+    while I.size > 0:
+        i = I[-1]
+        pick[counter] = i
+        counter += 1
+        idx = I[0:-1]
+        xx1 = np.maximum(x1[i], x1[idx])
+        yy1 = np.maximum(y1[i], y1[idx])
+        xx2 = np.minimum(x2[i], x2[idx])
+        yy2 = np.minimum(y2[i], y2[idx])
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        if method is 'Min':
+            o = inter / np.minimum(area[i], area[idx])
+        else:
+            o = inter / (area[i] + area[idx] - inter)
+        I = I[np.where(o <= threshold)]
+    pick = pick[0:counter]
+    return pick
+
+
+# function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)
+def pad(total_boxes, w, h):
+    """Compute the padding coordinates (pad the bounding boxes to square)"""
+    tmpw = (total_boxes[:, 2] - total_boxes[:, 0] + 1).astype(np.int32)
+    tmph = (total_boxes[:, 3] - total_boxes[:, 1] + 1).astype(np.int32)
+    numbox = total_boxes.shape[0]
+
+    dx = np.ones((numbox), dtype=np.int32)
+    dy = np.ones((numbox), dtype=np.int32)
+    edx = tmpw.copy().astype(np.int32)
+    edy = tmph.copy().astype(np.int32)
+
+    x = total_boxes[:, 0].copy().astype(np.int32)
+    y = total_boxes[:, 1].copy().astype(np.int32)
+    ex = total_boxes[:, 2].copy().astype(np.int32)
+    ey = total_boxes[:, 3].copy().astype(np.int32)
+
+    tmp = np.where(ex > w)
+    edx.flat[tmp] = np.expand_dims(-ex[tmp] + w + tmpw[tmp], 1)
+    ex[tmp] = w
+
+    tmp = np.where(ey > h)
+    edy.flat[tmp] = np.expand_dims(-ey[tmp] + h + tmph[tmp], 1)
+    ey[tmp] = h
+
+    tmp = np.where(x < 1)
+    dx.flat[tmp] = np.expand_dims(2 - x[tmp], 1)
+    x[tmp] = 1
+
+    tmp = np.where(y < 1)
+    dy.flat[tmp] = np.expand_dims(2 - y[tmp], 1)
+    y[tmp] = 1
+
+    return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
+
+
+# function [bboxA] = rerec(bboxA)
+def rerec(bboxA):
+    """Convert bboxA to square."""
+    h = bboxA[:, 3] - bboxA[:, 1]
+    w = bboxA[:, 2] - bboxA[:, 0]
+    l = np.maximum(w, h)
+    bboxA[:, 0] = bboxA[:, 0] + w * 0.5 - l * 0.5
+    bboxA[:, 1] = bboxA[:, 1] + h * 0.5 - l * 0.5
+    bboxA[:, 2:4] = bboxA[:, 0:2] + np.transpose(np.tile(l, (2, 1)))
+    return bboxA
+
+
+def imresample(img, sz):
+    im_data = cv2.resize(img, (sz[1], sz[0]), interpolation=cv2.INTER_AREA)  # @UndefinedVariable
+    return im_data
+
+    # This method is kept for debugging purpose
+#     h=img.shape[0]
+#     w=img.shape[1]
+#     hs, ws = sz
+#     dx = float(w) / ws
+#     dy = float(h) / hs
+#     im_data = np.zeros((hs,ws,3))
+#     for a1 in range(0,hs):
+#         for a2 in range(0,ws):
+#             for a3 in range(0,3):
+#                 im_data[a1,a2,a3] = img[int(floor(a1*dy)),int(floor(a2*dx)),a3]
+#     return im_data

fawkes/differentiator.py

@@ -47,7 +47,7 @@ class FawkesMaskGeneration:
                  max_iterations=MAX_ITERATIONS, initial_const=INITIAL_CONST,
                  intensity_range=INTENSITY_RANGE, l_threshold=L_THRESHOLD,
                  max_val=MAX_VAL, keep_final=KEEP_FINAL, maximize=MAXIMIZE, image_shape=IMAGE_SHAPE,
-                 verbose=0, ratio=RATIO, limit_dist=LIMIT_DIST):
+                 verbose=0, ratio=RATIO, limit_dist=LIMIT_DIST, faces=None):
 
         assert intensity_range in {'raw', 'imagenet', 'inception', 'mnist'}
 
@@ -69,10 +69,12 @@ class FawkesMaskGeneration:
         self.ratio = ratio
         self.limit_dist = limit_dist
         self.single_shape = list(image_shape)
+        self.faces = faces
 
         self.input_shape = tuple([self.batch_size] + self.single_shape)
 
         self.bottleneck_shape = tuple([self.batch_size] + self.single_shape)
+        # self.bottleneck_shape = tuple([self.batch_size, bottleneck_model_ls[0].output_shape[-1]])
 
         # the variable we're going to optimize over
         self.modifier = tf.Variable(np.zeros(self.input_shape, dtype=np.float32))
@@ -149,8 +151,6 @@ class FawkesMaskGeneration:
                      self.dist_raw,
                      tf.zeros_like(self.dist_raw)))
         self.dist_sum = tf.reduce_sum(tf.where(self.mask, self.dist, tf.zeros_like(self.dist)))
-        # self.dist_sum = 1e-5 * tf.reduce_sum(self.dist)
-        # self.dist_raw_sum = self.dist_sum
 
         def resize_tensor(input_tensor, model_input_shape):
             if input_tensor.shape[1:] == model_input_shape or model_input_shape[1] is None:
@@ -171,16 +171,14 @@ class FawkesMaskGeneration:
 
             self.bottleneck_a = bottleneck_model(cur_aimg_input)
             if self.MIMIC_IMG:
-                # cur_timg_input = resize_tensor(self.timg_input, model_input_shape)
-                # cur_simg_input = resize_tensor(self.simg_input, model_input_shape)
                 cur_timg_input = self.timg_input
                 cur_simg_input = self.simg_input
                 self.bottleneck_t = calculate_direction(bottleneck_model, cur_timg_input, cur_simg_input)
-                # self.bottleneck_t = bottleneck_model(cur_timg_input)
             else:
                 self.bottleneck_t = self.bottleneck_t_raw
 
             bottleneck_diff = self.bottleneck_t - self.bottleneck_a
+
             scale_factor = tf.sqrt(tf.reduce_sum(tf.square(self.bottleneck_t), axis=1))
 
             cur_bottlesim = tf.sqrt(tf.reduce_sum(tf.square(bottleneck_diff), axis=1))
@@ -189,7 +187,6 @@ class FawkesMaskGeneration:
 
             self.bottlesim += cur_bottlesim
 
-            # self.bottlesim_push += cur_bottlesim_push_sum
             self.bottlesim_sum += cur_bottlesim_sum
 
         # sum up the losses
@@ -202,20 +199,13 @@ class FawkesMaskGeneration:
                                                self.loss,
                                                tf.zeros_like(self.loss)))
 
-        # self.loss_sum = self.dist_sum + tf.reduce_sum(self.bottlesim)
-        # import pdb
-        # pdb.set_trace()
-        # self.loss_sum = tf.reduce_sum(tf.where(self.mask, self.loss, tf.zeros_like(self.loss)))
-
-        # Setup the Adadelta optimizer and keep track of variables
-        # we're creating
         start_vars = set(x.name for x in tf.global_variables())
         self.learning_rate_holder = tf.placeholder(tf.float32, shape=[])
+
         optimizer = tf.train.AdadeltaOptimizer(self.learning_rate_holder)
         # optimizer = tf.train.AdamOptimizer(self.learning_rate_holder)
 
-        self.train = optimizer.minimize(self.loss_sum,
+        self.train = optimizer.minimize(self.loss_sum, var_list=[self.modifier])
-                                        var_list=[self.modifier])
         end_vars = tf.global_variables()
         new_vars = [x for x in end_vars if x.name not in start_vars]
 
@@ -297,6 +287,7 @@ class FawkesMaskGeneration:
         LR = self.learning_rate
         nb_imgs = source_imgs.shape[0]
         mask = [True] * nb_imgs + [False] * (self.batch_size - nb_imgs)
+        # mask = [True] * self.batch_size
         mask = np.array(mask, dtype=np.bool)
 
         source_imgs = np.array(source_imgs)
@@ -317,19 +308,34 @@ class FawkesMaskGeneration:
             timg_tanh_batch = np.zeros(self.input_shape)
         else:
             timg_tanh_batch = np.zeros(self.bottleneck_shape)
+
         weights_batch = np.zeros(self.bottleneck_shape)
         simg_tanh_batch[:nb_imgs] = simg_tanh[:nb_imgs]
         timg_tanh_batch[:nb_imgs] = timg_tanh[:nb_imgs]
         weights_batch[:nb_imgs] = weights[:nb_imgs]
         modifier_batch = np.ones(self.input_shape) * 1e-6
 
-        self.sess.run(self.setup,
+        temp_images = []
-                      {self.assign_timg_tanh: timg_tanh_batch,
+
-                       self.assign_simg_tanh: simg_tanh_batch,
+        # set the variables so that we don't have to send them over again
-                       self.assign_const: CONST,
+        if self.MIMIC_IMG:
-                       self.assign_mask: mask,
+            self.sess.run(self.setup,
-                       self.assign_weights: weights_batch,
+                          {self.assign_timg_tanh: timg_tanh_batch,
-                       self.assign_modifier: modifier_batch})
+                           self.assign_simg_tanh: simg_tanh_batch,
+                           self.assign_const: CONST,
+                           self.assign_mask: mask,
+                           self.assign_weights: weights_batch,
+                           self.assign_modifier: modifier_batch})
+        else:
+            # if directly mimicking a vector, use assign_bottleneck_t_raw
+            # in setup
+            self.sess.run(self.setup,
+                          {self.assign_bottleneck_t_raw: timg_tanh_batch,
+                           self.assign_simg_tanh: simg_tanh_batch,
+                           self.assign_const: CONST,
+                           self.assign_mask: mask,
+                           self.assign_weights: weights_batch,
+                           self.assign_modifier: modifier_batch})
 
         best_bottlesim = [0] * nb_imgs if self.maximize else [np.inf] * nb_imgs
         best_adv = np.zeros_like(source_imgs)
@@ -347,6 +353,7 @@ class FawkesMaskGeneration:
                      dist_raw_sum,
                      bottlesim_sum / nb_imgs))
 
+        finished_idx = set()
         try:
             total_distance = [0] * nb_imgs
 
@@ -369,8 +376,14 @@ class FawkesMaskGeneration:
                     [self.dist_raw,
                      self.bottlesim,
                      self.aimg_input])
+
+                all_clear = True
                 for e, (dist_raw, bottlesim, aimg_input) in enumerate(
                         zip(dist_raw_list, bottlesim_list, aimg_input_list)):
+
+                    if e in finished_idx:
+                        continue
+
                     if e >= nb_imgs:
                         break
                     if (bottlesim < best_bottlesim[e] and bottlesim > total_distance[e] * 0.1 and (
@@ -379,40 +392,55 @@ class FawkesMaskGeneration:
                         best_bottlesim[e] = bottlesim
                         best_adv[e] = aimg_input
 
-                if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0:
+                    # if iteration > 20 and (dist_raw >= self.l_threshold or iteration == self.MAX_ITERATIONS - 1):
-                    # LR = LR / 2
+                    #     finished_idx.add(e)
+                    #     print("{} finished at dist {}".format(e, dist_raw))
+                    #     best_bottlesim[e] = bottlesim
+                    #     best_adv[e] = aimg_input
+                    #
+                    all_clear = False
+
+                if all_clear:
+                    break
+
+                if iteration != 0 and iteration % (self.MAX_ITERATIONS // 2) == 0:
+                    LR = LR / 2
                     print("Learning Rate: ", LR)
 
-                if iteration % (self.MAX_ITERATIONS // 10) == 0:
+                if iteration % (self.MAX_ITERATIONS // 5) == 0:
                     if self.verbose == 1:
-                        loss_sum = float(self.sess.run(self.loss_sum))
-                        dist_sum = float(self.sess.run(self.dist_sum))
-                        thresh_over = (dist_sum /
-                                       self.batch_size /
-                                       self.l_threshold *
-                                       100)
                         dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
                         bottlesim_sum = self.sess.run(self.bottlesim_sum)
-                        print('ITER %4d: Total loss: %.4E; perturb: %.6f (%.2f%% over, raw: %.6f); sim: %f'
+                        print('ITER %4d perturb: %.5f; sim: %f'
-                              % (iteration,
+                              % (iteration, dist_raw_sum / nb_imgs, bottlesim_sum / nb_imgs))
-                                 Decimal(loss_sum),
+
-                                 dist_sum,
+                        # protected_images = aimg_input_list
-                                 thresh_over,
+                        #
-                                 dist_raw_sum,
+                        # orginal_images = np.copy(self.faces.cropped_faces)
-                                 bottlesim_sum / nb_imgs))
+                        # cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(
+                        #     orginal_images)
+                        # final_images = self.faces.merge_faces(cloak_perturbation)
+                        #
+                        # for p_img, img in zip(protected_images, final_images):
+                        #     dump_image(reverse_process_cloaked(p_img),
+                        #                "/home/shansixioing/fawkes/data/emily/emily_cloaked_cropped{}.png".format(iteration),
+                        #                format='png')
+                        #
+                        #     dump_image(img,
+                        #                "/home/shansixioing/fawkes/data/emily/emily_cloaked_{}.png".format(iteration),
+                        #                format='png')
+
         except KeyboardInterrupt:
             pass
 
         if self.verbose == 1:
             loss_sum = float(self.sess.run(self.loss_sum))
             dist_sum = float(self.sess.run(self.dist_sum))
-            thresh_over = (dist_sum / self.batch_size / self.l_threshold * 100)
             dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
             bottlesim_sum = float(self.sess.run(self.bottlesim_sum))
-            print('END:       Total loss: %.4E; perturb: %.6f (%.2f%% over, raw: %.6f); sim: %f'
+            print('END:       Total loss: %.4E; perturb: %.6f (raw: %.6f); sim: %f'
                   % (Decimal(loss_sum),
                      dist_sum,
-                     thresh_over,
                      dist_raw_sum,
                      bottlesim_sum / nb_imgs))
 

fawkes/foo.jpg.REMOVED.git-id

@@ -0,0 +1 @@
+837da51fc1cd7e21f6989badd07c3ccec543833e

fawkes/protection.py

@@ -6,21 +6,16 @@ import sys
 
 import numpy as np
 from differentiator import FawkesMaskGeneration
-from keras.applications.vgg16 import preprocess_input
+from utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, \
-from keras.preprocessing import image
+    Faces
-from skimage.transform import resize
-from tensorflow import set_random_seed
-from utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked
 
 random.seed(12243)
 np.random.seed(122412)
-set_random_seed(12242)
 
-BATCH_SIZE = 1
+BATCH_SIZE = 10
-MAX_ITER = 1000
 
 
-def generate_cloak_images(sess, feature_extractors, image_X, target_X=None, th=0.01):
+def generate_cloak_images(sess, feature_extractors, image_X, target_emb=None, th=0.01, faces=None):
     batch_size = BATCH_SIZE if len(image_X) > BATCH_SIZE else len(image_X)
 
     differentiator = FawkesMaskGeneration(sess, feature_extractors,
@@ -29,92 +24,117 @@ def generate_cloak_images(sess, feature_extractors, image_X, target_X=None, th=0
                                           intensity_range='imagenet',
                                           initial_const=args.sd,
                                           learning_rate=args.lr,
-                                          max_iterations=MAX_ITER,
+                                          max_iterations=args.max_step,
                                           l_threshold=th,
-                                          verbose=1, maximize=False, keep_final=False, image_shape=image_X.shape[1:])
+                                          verbose=1, maximize=False, keep_final=False, image_shape=image_X.shape[1:],
+                                          faces=faces)
 
-    cloaked_image_X = differentiator.attack(image_X, target_X)
+    cloaked_image_X = differentiator.attack(image_X, target_emb)
     return cloaked_image_X
 
 
 def get_mode_config(mode):
     if mode == 'low':
         args.feature_extractor = "low_extract"
+        # args.th = 0.003
         args.th = 0.001
     elif mode == 'mid':
         args.feature_extractor = "mid_extract"
-        args.th = 0.001
+        args.th = 0.004
     elif mode == 'high':
         args.feature_extractor = "high_extract"
-        args.th = 0.005
+        args.th = 0.004
     elif mode == 'ultra':
         args.feature_extractor = "high_extract"
-        args.th = 0.007
+        args.th = 0.03
     elif mode == 'custom':
         pass
     else:
         raise Exception("mode must be one of 'low', 'mid', 'high', 'ultra', 'custom'")
 
 
-def extract_faces(img):
+def check_imgs(imgs):
-    #  wait on Huiying
+    if np.max(imgs) <= 1 and np.min(imgs) >= 0:
-    return preprocess_input(resize(img, (224, 224)))
+        imgs = imgs * 255.0
+    elif np.max(imgs) <= 255 and np.min(imgs) >= 0:
+        pass
+    else:
+        raise Exception("Image values ")
+    return imgs
 
 
 def fawkes():
+    assert args.format in ['png', 'jpg', 'jpeg']
+    if args.format == 'jpg':
+        args.format = 'jpeg'
     get_mode_config(args.mode)
 
     sess = init_gpu(args.gpu)
-    feature_extractors_ls = [load_extractor(args.feature_extractor)]
+    # feature_extractors_ls = [load_extractor(args.feature_extractor)]
+    # fs_names = ['mid_extract', 'high_extract']
+    fs_names = [args.feature_extractor]
+    feature_extractors_ls = [load_extractor(name) for name in fs_names]
 
     image_paths = glob.glob(os.path.join(args.directory, "*"))
     image_paths = [path for path in image_paths if "_cloaked" not in path.split("/")[-1]]
 
-    orginal_images = [extract_faces(image.img_to_array(image.load_img(cur_path))) for cur_path in
+    faces = Faces(image_paths, sess)
-                      image_paths]
 
+    orginal_images = faces.cropped_faces
     orginal_images = np.array(orginal_images)
 
-    if args.seperate_target:
+    if args.separate_target:
-        target_images = []
+        target_embedding = []
         for org_img in orginal_images:
             org_img = org_img.reshape([1] + list(org_img.shape))
-            tar_img = select_target_label(org_img, feature_extractors_ls, [args.feature_extractor])
+            tar_emb = select_target_label(org_img, feature_extractors_ls, fs_names)
-            target_images.append(tar_img)
+            target_embedding.append(tar_emb)
-        target_images = np.concatenate(target_images)
+        target_embedding = np.concatenate(target_embedding)
     else:
-        target_images = select_target_label(orginal_images, feature_extractors_ls, [args.feature_extractor])
+        target_embedding = select_target_label(orginal_images, feature_extractors_ls, fs_names)
 
     protected_images = generate_cloak_images(sess, feature_extractors_ls, orginal_images,
-                                             target_X=target_images, th=args.th)
+                                             target_emb=target_embedding, th=args.th, faces=faces)
 
-    for p_img, path in zip(protected_images, image_paths):
+    faces.cloaked_cropped_faces = protected_images
-        p_img = reverse_process_cloaked(p_img)
+
-        file_name = "{}_cloaked.jpeg".format(".".join(path.split(".")[:-1]))
+    cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(orginal_images)
-        dump_image(p_img, file_name, format="JPEG")
+    final_images = faces.merge_faces(cloak_perturbation)
+
+    for p_img, cloaked_img, path in zip(final_images, protected_images, image_paths):
+        file_name = "{}_{}_{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), args.mode, args.th,
+                                                     args.feature_extractor, args.format)
+        dump_image(p_img, file_name, format=args.format)
+        #
+        # file_name = "{}_{}_{}_{}_cloaked_cropped.png".format(".".join(path.split(".")[:-1]), args.mode, args.th,
+        #                                                      args.feature_extractor)
+        # dump_image(reverse_process_cloaked(cloaked_img), file_name, format="png")
 
 
 def parse_arguments(argv):
     parser = argparse.ArgumentParser()
-    parser.add_argument('--directory', type=str,
+    parser.add_argument('--directory', '-d', type=str,
                         help='directory that contain images for cloaking', default='imgs/')
 
     parser.add_argument('--gpu', type=str,
                         help='GPU id', default='0')
 
     parser.add_argument('--mode', type=str,
-                        help='cloak generation mode', default='mid')
+                        help='cloak generation mode', default='high')
     parser.add_argument('--feature-extractor', type=str,
                         help="name of the feature extractor used for optimization",
-                        default="mid_extract")
+                        default="high_extract")
 
-    parser.add_argument('--th', type=float, default=0.005)
+    parser.add_argument('--th', type=float, default=0.01)
+    parser.add_argument('--max-step', type=int, default=200)
     parser.add_argument('--sd', type=int, default=1e9)
-    parser.add_argument('--lr', type=float, default=1)
+    parser.add_argument('--lr', type=float, default=10)
 
-    parser.add_argument('--result_directory', type=str, default="../results")
+    parser.add_argument('--separate_target', action='store_true')
-    parser.add_argument('--seperate_target', action='store_true')
 
+    parser.add_argument('--format', type=str,
+                        help="final image format",
+                        default="jpg")
     return parser.parse_args(argv)
 
 

fawkes/utils.py

@@ -1,3 +1,5 @@
+import glob
+import gzip
 import json
 import os
 import pickle
@@ -7,12 +9,16 @@ import keras
 import keras.backend as K
 import numpy as np
 import tensorflow as tf
+from align_face import align, aligner
 from keras.applications.vgg16 import preprocess_input
 from keras.layers import Dense, Activation
 from keras.models import Model
 from keras.preprocessing import image
+from keras.utils import get_file
 from keras.utils import to_categorical
+from skimage.transform import resize
 from sklearn.metrics import pairwise_distances
+from PIL import Image, ExifTags
 
 
 def clip_img(X, preprocessing='raw'):
@@ -22,6 +28,86 @@ def clip_img(X, preprocessing='raw'):
     return X
 
 
+def load_image(path):
+    img = Image.open(path)
+    if img._getexif() is not None:
+        for orientation in ExifTags.TAGS.keys():
+            if ExifTags.TAGS[orientation] == 'Orientation':
+                break
+
+        exif = dict(img._getexif().items())
+        if orientation in exif.keys():
+            if exif[orientation] == 3:
+                img = img.rotate(180, expand=True)
+            elif exif[orientation] == 6:
+                img = img.rotate(270, expand=True)
+            elif exif[orientation] == 8:
+                img = img.rotate(90, expand=True)
+            else:
+                pass
+    img = img.convert('RGB')
+    image_array = image.img_to_array(img)
+
+    return image_array
+
+
+class Faces(object):
+    def __init__(self, image_paths, sess):
+        self.aligner = aligner(sess)
+        self.org_faces = []
+        self.cropped_faces = []
+        self.cropped_faces_shape = []
+        self.cropped_index = []
+        self.callback_idx = []
+        for i, p in enumerate(image_paths):
+            cur_img = load_image(p)
+            self.org_faces.append(cur_img)
+            align_img = align(cur_img, self.aligner, margin=0.7)
+            cur_faces = align_img[0]
+
+            cur_shapes = [f.shape[:-1] for f in cur_faces]
+
+            cur_faces_square = []
+            for img in cur_faces:
+                long_size = max([img.shape[1], img.shape[0]])
+                base = np.zeros((long_size, long_size, 3))
+                base[0:img.shape[0], 0:img.shape[1], :] = img
+                cur_faces_square.append(base)
+
+            cur_index = align_img[1]
+            cur_faces_square = [resize(f, (224, 224)) for f in cur_faces_square]
+            self.cropped_faces_shape.extend(cur_shapes)
+            self.cropped_faces.extend(cur_faces_square)
+            self.cropped_index.extend(cur_index)
+            self.callback_idx.extend([i] * len(cur_faces_square))
+
+        self.cropped_faces = preprocess_input(np.array(self.cropped_faces))
+        self.cloaked_cropped_faces = None
+        self.cloaked_faces = np.copy(self.org_faces)
+
+    def get_faces(self):
+        return self.cropped_faces
+
+    def merge_faces(self, cloaks):
+        # import pdb
+        # pdb.set_trace()
+
+        self.cloaked_faces = np.copy(self.org_faces)
+
+        for i in range(len(self.cropped_faces)):
+            cur_cloak = cloaks[i]
+            org_shape = self.cropped_faces_shape[i]
+            old_square_shape = max([org_shape[0], org_shape[1]])
+            reshape_cloak = resize(cur_cloak, (old_square_shape, old_square_shape))
+            reshape_cloak = reshape_cloak[0:org_shape[0], 0:org_shape[1], :]
+
+            callback_id = self.callback_idx[i]
+            bb = self.cropped_index[i]
+            self.cloaked_faces[callback_id][bb[1]:bb[3], bb[0]:bb[2], :] += reshape_cloak
+
+        return self.cloaked_faces
+
+
 def dump_dictionary_as_json(dict, outfile):
     j = json.dumps(dict)
     with open(outfile, "wb") as f:
@@ -30,10 +116,12 @@ def dump_dictionary_as_json(dict, outfile):
 
 def fix_gpu_memory(mem_fraction=1):
     os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_fraction)
+    tf_config = None
-    tf_config = tf.ConfigProto(gpu_options=gpu_options)
+    if tf.test.is_gpu_available():
-    tf_config.gpu_options.allow_growth = True
+        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_fraction)
-    tf_config.log_device_placement = False
+        tf_config = tf.ConfigProto(gpu_options=gpu_options)
+        tf_config.gpu_options.allow_growth = True
+        tf_config.log_device_placement = False
     init_op = tf.global_variables_initializer()
     sess = tf.Session(config=tf_config)
     sess.run(init_op)
@@ -45,7 +133,6 @@ def load_victim_model(number_classes, teacher_model=None, end2end=False):
     for l in teacher_model.layers:
         l.trainable = end2end
     x = teacher_model.layers[-1].output
-
     x = Dense(number_classes)(x)
     x = Activation('softmax', name="act")(x)
     model = Model(teacher_model.input, x)
@@ -141,6 +228,7 @@ def imagenet_preprocessing(x, data_format=None):
 
     return x
 
+
 def imagenet_reverse_preprocessing(x, data_format=None):
     import keras.backend as K
     x = np.array(x)
@@ -185,7 +273,20 @@ def build_bottleneck_model(model, cut_off):
 
 
 def load_extractor(name):
-    model = keras.models.load_model("../feature_extractors/{}.h5".format(name))
+    model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
+    os.makedirs(model_dir, exist_ok=True)
+    model_file = os.path.join(model_dir, "{}.h5".format(name))
+    if os.path.exists(model_file):
+        model = keras.models.load_model(model_file)
+    else:
+        get_file("{}.h5".format(name), "http://sandlab.cs.uchicago.edu/fawkes/files/{}.h5".format(name),
+                 cache_dir=model_dir, cache_subdir='')
+
+        get_file("{}_emb.p.gz".format(name), "http://sandlab.cs.uchicago.edu/fawkes/files/{}_emb.p.gz".format(name),
+                 cache_dir=model_dir, cache_subdir='')
+
+        model = keras.models.load_model(model_file)
+
     if hasattr(model.layers[-1], "activation") and model.layers[-1].activation == "softmax":
         raise Exception(
             "Given extractor's last layer is softmax, need to remove the top layers to make it into a feature extractor")
@@ -199,11 +300,13 @@ def load_extractor(name):
     return model
 
 
+
 def get_dataset_path(dataset):
-    if not os.path.exists("config.json"):
+    model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
+    if not os.path.exists(os.path.join(model_dir, "config.json")):
         raise Exception("Please config the datasets before running protection code. See more in README and config.py.")
 
-    config = json.load(open("config.json", 'r'))
+    config = json.load(open(os.path.join(model_dir, "config.json"), 'r'))
     if dataset not in config:
         raise Exception(
             "Dataset {} does not exist, please download to data/ and add the path to this function... Abort".format(
@@ -217,7 +320,8 @@ def normalize(x):
 
 
 def dump_image(x, filename, format="png", scale=False):
-    img = image.array_to_img(x, scale=scale)
+    # img = image.array_to_img(x, scale=scale)
+    img = image.array_to_img(x)
     img.save(filename, format)
     return
 
@@ -235,9 +339,13 @@ def load_dir(path):
 
 
 def load_embeddings(feature_extractors_names):
+    model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
     dictionaries = []
     for extractor_name in feature_extractors_names:
-        path2emb = pickle.load(open("../feature_extractors/embeddings/{}_emb_norm.p".format(extractor_name), "rb"))
+        fp = gzip.open(os.path.join(model_dir, "{}_emb.p.gz".format(extractor_name)), 'rb')
+        path2emb = pickle.load(fp)
+        fp.close()
+
         dictionaries.append(path2emb)
 
     merge_dict = {}
@@ -272,6 +380,8 @@ def calculate_dist_score(a, b, feature_extractors_ls, metric='l2'):
 
 
 def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, metric='l2'):
+    model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
+
     original_feature_x = extractor_ls_predict(feature_extractors_ls, imgs)
 
     path2emb = load_embeddings(feature_extractors_names)
@@ -282,37 +392,25 @@ def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, m
 
     pair_dist = pairwise_distances(original_feature_x, embs, metric)
     max_sum = np.min(pair_dist, axis=0)
-    sorted_idx = np.argsort(max_sum)[::-1]
+    max_id = np.argmax(max_sum)
 
-    highest_num = 0
+    image_paths = glob.glob(os.path.join(model_dir, "target_data/{}/*".format(paths[int(max_id)])))
-    paired_target_X = None
+    target_images = [image.img_to_array(image.load_img(cur_path)) for cur_path in
-    final_target_class_path = None
+                     image_paths]
-    for idx in sorted_idx[:1]:
+    target_images = preprocess_input(np.array([resize(x, (224, 224)) for x in target_images]))
-        target_class_path = paths[idx]
-        cur_target_X = load_dir(target_class_path)
-        cur_target_X = np.concatenate([cur_target_X, cur_target_X, cur_target_X])
-        cur_tot_sum, cur_paired_target_X = calculate_dist_score(imgs, cur_target_X,
-                                                                feature_extractors_ls,
-                                                                metric=metric)
-        if cur_tot_sum > highest_num:
-            highest_num = cur_tot_sum
-            paired_target_X = cur_paired_target_X
 
-    np.random.shuffle(paired_target_X)
+    target_images = list(target_images)
-    paired_target_X = list(paired_target_X)
+    while len(target_images) < len(imgs):
-    while len(paired_target_X) < len(imgs):
+        target_images += target_images
-        paired_target_X += paired_target_X
-
-    paired_target_X = paired_target_X[:len(imgs)]
-    return np.array(paired_target_X)
 
+    target_images = random.sample(target_images, len(imgs))
+    return np.array(target_images)
 
 
 class CloakData(object):
     def __init__(self, protect_directory=None, img_shape=(224, 224)):
 
         self.img_shape = img_shape
-
         # self.train_data_dir, self.test_data_dir, self.number_classes, self.number_samples = get_dataset_path(dataset)
         # self.all_labels = sorted(list(os.listdir(self.train_data_dir)))
         self.protect_directory = protect_directory

fawkes_dev/README.md

@@ -52,6 +52,7 @@ We shared three different feature extractors under feature_extractors/
 1. low_extract.h5: trained on WebFace dataset with DenseNet architecture. 
 2. mid_extract.h5: VGGFace2 dataset with DenseNet architecture. Trained with PGD adversarial training for 5 epochs. 
 3. high_extract.h5: WebFace dataset with DenseNet architecture. Trained with PGD adversarial training for 20 epochs. 
+4. high2_extract.h5: VGGFace2 dataset with DenseNet architecture. Trained with PGD adversarial training for 20 epochs. 
 
 ### Citation
 ```

fawkes_dev/azure.py

@@ -0,0 +1,399 @@
+
+import http.client, urllib.request, urllib.parse, urllib.error
+import json
+import time
+
+#Face API Key and Endpoint
+subscription_key = 'e127e26e4d534e2bad6fd9ca06145302'
+uri_base = 'eastus.api.cognitive.microsoft.com'
+# uri_base = 'https://shawn.cognitiveservices.azure.com/'
+
+def detect_face(image_url):
+    headers = {
+        # Request headers
+        'Content-Type': 'application/json',
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+        # Request parameters
+        'returnFaceId': 'true',
+        'returnFaceLandmarks': 'false',
+        'recognitionModel': 'recognition_01',
+        'returnRecognitionModel': 'false',
+        'detectionModel': 'detection_01',
+    })
+
+    body = json.dumps({
+        'url': image_url
+    })
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("POST", "/face/v1.0/detect?%s" % params, body, headers)
+    response = conn.getresponse()
+    data = json.loads(response.read())
+    conn.close()
+    return data[0]["faceId"]
+
+
+def verify_face(faceId, personGroupId, personId):
+    # html header
+    headers = {
+        'Content-Type': 'application/json',
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    # image URL
+    body = json.dumps({
+        "faceId": faceId,
+        "personId": personId,
+        "PersonGroupId": personGroupId
+    })
+
+    # Call Face API
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("POST", "/face/v1.0/verify?%s" % params, body, headers)
+    response = conn.getresponse()
+    data = json.loads(response.read())
+    conn.close()
+    return data
+
+
+def create_personGroupId(personGroupId, personGroupName):
+    headers = {
+        # Request headers
+        'Content-Type': 'application/json',
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({
+        "name": personGroupName
+    })
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("PUT", "/face/v1.0/persongroups/{}?%s".format(personGroupId) % params, body, headers)
+    response = conn.getresponse()
+    data = response.read()
+    print(data)
+    conn.close()
+
+
+def create_personId(personGroupId, personName):
+    headers = {
+        # Request headers
+        'Content-Type': 'application/json',
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({
+        "name": personName
+    })
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("POST", "/face/v1.0/persongroups/{}/persons?%s".format(personGroupId) % params, body, headers)
+    response = conn.getresponse()
+    data = json.loads(response.read())
+    print(data)
+    conn.close()
+    return data["personId"]
+
+
+def add_persistedFaceId(personGroupId, personId, image_url):
+    headers = {
+        'Content-Type': 'application/json',
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+        'personGroupId': personGroupId,
+        'personId': personId
+    })
+
+    body = json.dumps({
+        'url': image_url
+    })
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("POST", "/face/v1.0/persongroups/{}/persons/{}/persistedFaces?%s".format(personGroupId, personId) % params, body, headers)
+    response = conn.getresponse()
+    data = json.loads(response.read())
+    print(data)
+    conn.close()
+    return data["persistedFaceId"]
+
+
+def list_personGroupPerson(personGroupId):
+    headers = {
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({})
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("GET", "/face/v1.0/persongroups/{}/persons?%s".format(personGroupId) % params, body, headers)
+    response = conn.getresponse()
+    data = json.loads(response.read())
+    conn.close()
+    for person in data:
+        print(person["personId"], len(person["persistedFaceIds"]))
+
+
+def get_personGroupPerson(personGroupId, personId):
+    headers = {
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({})
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("GET", "/face/v1.0/persongroups/{}/persons/{}?%s".format(personGroupId, personId) % params, body, headers)
+    response = conn.getresponse()
+    data = json.loads(response.read())
+    print(data)
+    conn.close()
+
+
+def train_personGroup(personGroupId):
+    headers = {
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({})
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("POST", "/face/v1.0/persongroups/{}/train?%s".format(personGroupId) % params, body, headers)
+    response = conn.getresponse()
+    data = response.read()
+    print(data)
+    conn.close()
+
+
+def eval(original_faceIds, personGroupId, protect_personId):
+    headers = {
+        'Content-Type': 'application/json',
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({
+        'faceIds': original_faceIds,
+        'personGroupId': personGroupId,
+        'maxNumOfCandidatesReturned': 1
+    })
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("POST", "/face/v1.0/identify?%s" % params, body, headers)
+    response = conn.getresponse()
+    data = json.loads(response.read())
+    conn.close()
+
+    face = data[0]
+    if len(face["candidates"]) and face["candidates"][0]["personId"] == protect_personId:
+        return True
+    else:
+        return False
+
+
+def delete_personGroupPerson(personGroupId, personId):
+    headers = {
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({})
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("DELETE", "/face/v1.0/persongroups/{}/persons/{}?%s".format(personGroupId, personId) % params, body, headers)
+    response = conn.getresponse()
+    data = response.read()
+    print(data)
+    conn.close()
+
+
+def add_protect_person(personGroupId, name):
+    personId = create_personId(personGroupId, name)
+    for idx in range(72):
+        cloaked_image_url = "https://super.cs.uchicago.edu/~shawn/cloaked/{}_c.png".format(idx)
+        add_persistedFaceId(personGroupId, personId, cloaked_image_url)
+
+
+def add_sybil_person(personGroupId, name):
+    personId = create_personId(personGroupId, name)
+    for idx in range(82):
+        try:
+            cloaked_image_url = "https://super.cs.uchicago.edu/~shawn/sybils/{}_c.png".format(idx)
+            add_persistedFaceId(personGroupId, personId, cloaked_image_url)
+        except:
+            print(idx)
+
+
+def add_other_person(personGroupId):
+    for idx_person in range(65):
+        personId = create_personId(personGroupId, str(idx_person))
+        for idx_image in range(90):
+            try:
+                image_url = "https://super.cs.uchicago.edu/~shawn/train/{}/{}.png".format(idx_person, idx_image)
+                add_persistedFaceId(personGroupId, personId, image_url)
+            except:
+                print(idx_person, idx_image)
+
+
+def get_trainStatus(personGroupId):
+    headers = {
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({})
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("GET", "/face/v1.0/persongroups/{}/training?%s".format(personGroupId) % params, body, headers)
+    response = conn.getresponse()
+    data = response.read()
+    print(data)
+    conn.close()
+
+
+def test_original():
+    personGroupId = 'pubfig'
+    # create_personGroupId(personGroupId, 'pubfig')
+    # add protect person
+    protect_personId = 'd3df3012-6f3f-4c1b-b86d-55e91a352e01'
+    #protect_personId = create_personId(personGroupId, 'Emily')
+    #for idx in range(50):
+    #    image_url = "https://super.cs.uchicago.edu/~shawn/cloaked/{}_o.png".format(idx)
+    #    add_persistedFaceId(personGroupId, protect_personId, image_url)
+
+    # add other people
+    #for idx_person in range(65):
+    #    personId = create_personId(personGroupId, str(idx_person))
+    #    for idx_image in range(50):
+    #        try:
+    #            image_url = "https://super.cs.uchicago.edu/~shawn/train/{}/{}.png".format(idx_person, idx_image)
+    #            add_persistedFaceId(personGroupId, personId, image_url)
+    #        except:
+    #            print(idx_person, idx_image)
+
+
+    # train model based on personGroup
+    #train_personGroup(personGroupId)
+    #time.sleep(3)
+    #get_trainStatus(personGroupId)
+    #list_personGroupPerson(personGroupId)
+
+    idx_range = range(50, 82)
+    acc = 0.
+
+    for idx in idx_range:
+        original_image_url = "https://super.cs.uchicago.edu/~shawn/cloaked/{}_o.png".format(idx)
+        faceId = detect_face(original_image_url)
+        original_faceIds = [faceId]
+
+        # verify
+        res = eval(original_faceIds, personGroupId, protect_personId)
+        if res:
+            acc += 1.
+
+    acc /= len(idx_range)
+    print(acc) # 1.0
+
+
+def list_personGroups():
+    headers = {
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({})
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("GET", "/face/v1.0/persongroups?%s" % params, body, headers)
+    response = conn.getresponse()
+    data = response.read()
+    print(data)
+    conn.close()
+
+
+def delete_personGroup(personGroupId):
+    headers = {
+        'Ocp-Apim-Subscription-Key': subscription_key,
+    }
+
+    params = urllib.parse.urlencode({
+    })
+
+    body = json.dumps({})
+
+    conn = http.client.HTTPSConnection(uri_base)
+    conn.request("DELETE", "/face/v1.0/persongroups/{}?%s".format(personGroupId) % params, body, headers)
+    response = conn.getresponse()
+    data = response.read()
+    print(data)
+    conn.close()
+
+
+
+def main():
+    # delete_personGroup('cloaking')
+    # delete_personGroup('cloaking-emily')
+    # delete_personGroup('pubfig')
+    # list_personGroups()
+    # exit()
+    personGroupId = 'cloaking'
+    # create_personGroupId(personGroupId, 'cloaking')
+    list_personGroups()
+    exit()
+    #delete_personGroupPerson(personGroupId, '0ac606cd-24b3-440f-866a-31adf2a1b446')
+    #add_protect_person(personGroupId, 'Emily')
+    #personId = create_personId(personGroupId, 'Emily')
+    #add_sybil_person(personGroupId, 'sybil')
+    protect_personId = '6c5a71eb-f39a-4570-b3f5-72cca3ab5a6b'
+    #delete_personGroupPerson(personGroupId, protect_personId)
+    #add_protect_person(personGroupId, 'Emily')
+
+    # train model based on personGroup
+    #train_personGroup(personGroupId)
+    get_trainStatus(personGroupId)
+    #add_other_person(personGroupId)
+    #list_personGroupPerson(personGroupId)
+    #delete_personGroupPerson(personGroupId, '80e32c80-bc69-416a-9dff-c8d42d7a3301')
+
+    idx_range = range(72, 82)
+    original_faceIds = []
+    for idx in idx_range:
+        original_image_url = "https://super.cs.uchicago.edu/~shawn/cloaked/{}_o.png".format(idx)
+        faceId = detect_face(original_image_url)
+        original_faceIds.append(faceId)
+
+        # verify
+        eval(original_faceIds, personGroupId, protect_personId)
+
+
+if __name__ == '__main__':
+    main()

fawkes_dev/config.py

@@ -4,7 +4,7 @@ import os
 
 DATASETS = {
     "pubfig": "../data/pubfig",
-    "scrub": "/home/shansixioing/cloak/fawkes/data/scrub/",
+    "scrub": "/home/shansixioing/fawkes/data/scrub/",
     "vggface2": "/mnt/data/sixiongshan/data/vggface2/",
     "webface": "/mnt/data/sixiongshan/data/webface/",
     "youtubeface": "/mnt/data/sixiongshan/data/youtubeface/keras_flow_data/",
@@ -32,7 +32,8 @@ def main():
                            "num_images": num_images}
         print("Successfully config {}".format(dataset))
     j = json.dumps(config)
-    with open("config.json", "wb") as f:
+    model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
+    with open(os.path.join(model_dir, "config.json"), "wb") as f:
         f.write(j.encode())
 
 

fawkes_dev/differentiator.py

@@ -1,430 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Date    : 2020-05-17
-# @Author  : Shawn Shan (shansixiong@cs.uchicago.edu)
-# @Link    : https://www.shawnshan.com/
-
-import datetime
-import time
-from decimal import Decimal
-
-import numpy as np
-import tensorflow as tf
-from utils import preprocess, reverse_preprocess
-
-
-class FawkesMaskGeneration:
-    # if the attack is trying to mimic a target image or a neuron vector
-    MIMIC_IMG = True
-    # number of iterations to perform gradient descent
-    MAX_ITERATIONS = 10000
-    # larger values converge faster to less accurate results
-    LEARNING_RATE = 1e-2
-    # the initial constant c to pick as a first guess
-    INITIAL_CONST = 1
-    # pixel intensity range
-    INTENSITY_RANGE = 'imagenet'
-    # threshold for distance
-    L_THRESHOLD = 0.03
-    # whether keep the final result or the best result
-    KEEP_FINAL = False
-    # max_val of image
-    MAX_VAL = 255
-    # The following variables are used by DSSIM, should keep as default
-    # filter size in SSIM
-    FILTER_SIZE = 11
-    # filter sigma in SSIM
-    FILTER_SIGMA = 1.5
-    # weights used in MS-SSIM
-    SCALE_WEIGHTS = None
-    MAXIMIZE = False
-    IMAGE_SHAPE = (224, 224, 3)
-    RATIO = 1.0
-    LIMIT_DIST = False
-
-    def __init__(self, sess, bottleneck_model_ls, mimic_img=MIMIC_IMG,
-                 batch_size=1, learning_rate=LEARNING_RATE,
-                 max_iterations=MAX_ITERATIONS, initial_const=INITIAL_CONST,
-                 intensity_range=INTENSITY_RANGE, l_threshold=L_THRESHOLD,
-                 max_val=MAX_VAL, keep_final=KEEP_FINAL, maximize=MAXIMIZE, image_shape=IMAGE_SHAPE,
-                 verbose=0, ratio=RATIO, limit_dist=LIMIT_DIST):
-
-        assert intensity_range in {'raw', 'imagenet', 'inception', 'mnist'}
-
-        # constant used for tanh transformation to avoid corner cases
-        self.tanh_constant = 2 - 1e-6
-        self.sess = sess
-        self.MIMIC_IMG = mimic_img
-        self.LEARNING_RATE = learning_rate
-        self.MAX_ITERATIONS = max_iterations
-        self.initial_const = initial_const
-        self.batch_size = batch_size
-        self.intensity_range = intensity_range
-        self.l_threshold = l_threshold
-        self.max_val = max_val
-        self.keep_final = keep_final
-        self.verbose = verbose
-        self.maximize = maximize
-        self.learning_rate = learning_rate
-        self.ratio = ratio
-        self.limit_dist = limit_dist
-        self.single_shape = list(image_shape)
-
-        self.input_shape = tuple([self.batch_size] + self.single_shape)
-
-        self.bottleneck_shape = tuple([self.batch_size] + self.single_shape)
-
-        # the variable we're going to optimize over
-        self.modifier = tf.Variable(np.zeros(self.input_shape, dtype=np.float32))
-
-        # target image in tanh space
-        if self.MIMIC_IMG:
-            self.timg_tanh = tf.Variable(np.zeros(self.input_shape), dtype=np.float32)
-        else:
-            self.bottleneck_t_raw = tf.Variable(np.zeros(self.bottleneck_shape), dtype=np.float32)
-        # source image in tanh space
-        self.simg_tanh = tf.Variable(np.zeros(self.input_shape), dtype=np.float32)
-
-        self.const = tf.Variable(np.ones(batch_size), dtype=np.float32)
-        self.mask = tf.Variable(np.ones((batch_size), dtype=np.bool))
-        self.weights = tf.Variable(np.ones(self.bottleneck_shape,
-                                           dtype=np.float32))
-
-        # and here's what we use to assign them
-        self.assign_modifier = tf.placeholder(tf.float32, self.input_shape)
-        if self.MIMIC_IMG:
-            self.assign_timg_tanh = tf.placeholder(
-                tf.float32, self.input_shape)
-        else:
-            self.assign_bottleneck_t_raw = tf.placeholder(
-                tf.float32, self.bottleneck_shape)
-        self.assign_simg_tanh = tf.placeholder(tf.float32, self.input_shape)
-        self.assign_const = tf.placeholder(tf.float32, (batch_size))
-        self.assign_mask = tf.placeholder(tf.bool, (batch_size))
-        self.assign_weights = tf.placeholder(tf.float32, self.bottleneck_shape)
-
-        # the resulting image, tanh'd to keep bounded from -0.5 to 0.5
-        # adversarial image in raw space
-        self.aimg_raw = (tf.tanh(self.modifier + self.simg_tanh) /
-                         self.tanh_constant +
-                         0.5) * 255.0
-        # source image in raw space
-        self.simg_raw = (tf.tanh(self.simg_tanh) /
-                         self.tanh_constant +
-                         0.5) * 255.0
-        if self.MIMIC_IMG:
-            # target image in raw space
-            self.timg_raw = (tf.tanh(self.timg_tanh) /
-                             self.tanh_constant +
-                             0.5) * 255.0
-
-        # convert source and adversarial image into input space
-        if self.intensity_range == 'imagenet':
-            mean = tf.constant(np.repeat([[[[103.939, 116.779, 123.68]]]], self.batch_size, axis=0), dtype=tf.float32,
-                               name='img_mean')
-            self.aimg_input = (self.aimg_raw[..., ::-1] - mean)
-            self.simg_input = (self.simg_raw[..., ::-1] - mean)
-            if self.MIMIC_IMG:
-                self.timg_input = (self.timg_raw[..., ::-1] - mean)
-
-        elif self.intensity_range == 'raw':
-            self.aimg_input = self.aimg_raw
-            self.simg_input = self.simg_raw
-            if self.MIMIC_IMG:
-                self.timg_input = self.timg_raw
-
-        def batch_gen_DSSIM(aimg_raw_split, simg_raw_split):
-            msssim_split = tf.image.ssim(aimg_raw_split, simg_raw_split, max_val=255.0)
-            dist = (1.0 - tf.stack(msssim_split)) / 2.0
-            return dist
-
-        # raw value of DSSIM distance
-        self.dist_raw = batch_gen_DSSIM(self.aimg_raw, self.simg_raw)
-        # distance value after applying threshold
-        self.dist = tf.maximum(self.dist_raw - self.l_threshold, 0.0)
-
-        self.dist_raw_sum = tf.reduce_sum(
-            tf.where(self.mask,
-                     self.dist_raw,
-                     tf.zeros_like(self.dist_raw)))
-        self.dist_sum = tf.reduce_sum(tf.where(self.mask, self.dist, tf.zeros_like(self.dist)))
-
-        def resize_tensor(input_tensor, model_input_shape):
-            if input_tensor.shape[1:] == model_input_shape or model_input_shape[1] is None:
-                return input_tensor
-            resized_tensor = tf.image.resize(input_tensor, model_input_shape[:2])
-            return resized_tensor
-
-        def calculate_direction(bottleneck_model, cur_timg_input, cur_simg_input):
-            target_features = bottleneck_model(cur_timg_input)
-            return target_features
-            # target_center = tf.reduce_mean(target_features, axis=0)
-            # original = bottleneck_model(cur_simg_input)
-            # original_center = tf.reduce_mean(original, axis=0)
-            # direction = target_center - original_center
-            # final_target = original + self.ratio * direction
-            # return final_target
-
-        self.bottlesim = 0.0
-        self.bottlesim_sum = 0.0
-        self.bottlesim_push = 0.0
-        for bottleneck_model in bottleneck_model_ls:
-            model_input_shape = bottleneck_model.input_shape[1:]
-            cur_aimg_input = resize_tensor(self.aimg_input, model_input_shape)
-
-            self.bottleneck_a = bottleneck_model(cur_aimg_input)
-            if self.MIMIC_IMG:
-                # cur_timg_input = resize_tensor(self.timg_input, model_input_shape)
-                # cur_simg_input = resize_tensor(self.simg_input, model_input_shape)
-                cur_timg_input = self.timg_input
-                cur_simg_input = self.simg_input
-                self.bottleneck_t = calculate_direction(bottleneck_model, cur_timg_input, cur_simg_input)
-                # self.bottleneck_t = bottleneck_model(cur_timg_input)
-            else:
-                self.bottleneck_t = self.bottleneck_t_raw
-
-            bottleneck_diff = self.bottleneck_t - self.bottleneck_a
-            scale_factor = tf.sqrt(tf.reduce_sum(tf.square(self.bottleneck_t), axis=1))
-
-            cur_bottlesim = tf.sqrt(tf.reduce_sum(tf.square(bottleneck_diff), axis=1))
-            cur_bottlesim = cur_bottlesim / scale_factor
-            cur_bottlesim_sum = tf.reduce_sum(cur_bottlesim)
-
-            self.bottlesim += cur_bottlesim
-
-            # self.bottlesim_push += cur_bottlesim_push_sum
-            self.bottlesim_sum += cur_bottlesim_sum
-
-        # sum up the losses
-        if self.maximize:
-            self.loss = self.const * tf.square(self.dist) - self.bottlesim
-        else:
-            self.loss = self.const * tf.square(self.dist) + self.bottlesim
-
-        self.loss_sum = tf.reduce_sum(tf.where(self.mask, self.loss, tf.zeros_like(self.loss)))
-
-        # Setup the Adadelta optimizer and keep track of variables
-        # we're creating
-        start_vars = set(x.name for x in tf.global_variables())
-        self.learning_rate_holder = tf.placeholder(tf.float32, shape=[])
-        optimizer = tf.train.AdadeltaOptimizer(self.learning_rate_holder)
-
-        self.train = optimizer.minimize(self.loss_sum,
-                                        var_list=[self.modifier])
-        end_vars = tf.global_variables()
-        new_vars = [x for x in end_vars if x.name not in start_vars]
-
-        # these are the variables to initialize when we run
-        self.setup = []
-        self.setup.append(self.modifier.assign(self.assign_modifier))
-        if self.MIMIC_IMG:
-            self.setup.append(self.timg_tanh.assign(self.assign_timg_tanh))
-        else:
-            self.setup.append(self.bottleneck_t_raw.assign(
-                self.assign_bottleneck_t_raw))
-        self.setup.append(self.simg_tanh.assign(self.assign_simg_tanh))
-        self.setup.append(self.const.assign(self.assign_const))
-        self.setup.append(self.mask.assign(self.assign_mask))
-        self.setup.append(self.weights.assign(self.assign_weights))
-
-        self.init = tf.variables_initializer(var_list=[self.modifier] + new_vars)
-
-        print('Attacker loaded')
-
-    def preprocess_arctanh(self, imgs):
-
-        imgs = reverse_preprocess(imgs, self.intensity_range)
-        imgs /= 255.0
-        imgs -= 0.5
-        imgs *= self.tanh_constant
-        tanh_imgs = np.arctanh(imgs)
-
-        return tanh_imgs
-
-    def clipping(self, imgs):
-
-        imgs = reverse_preprocess(imgs, self.intensity_range)
-        imgs = np.clip(imgs, 0, self.max_val)
-        imgs = np.rint(imgs)
-
-        imgs = preprocess(imgs, self.intensity_range)
-
-        return imgs
-
-    def attack(self, source_imgs, target_imgs, weights=None):
-
-        if weights is None:
-            weights = np.ones([source_imgs.shape[0]] +
-                              list(self.bottleneck_shape[1:]))
-
-        assert weights.shape[1:] == self.bottleneck_shape[1:]
-        assert source_imgs.shape[1:] == self.input_shape[1:]
-        assert source_imgs.shape[0] == weights.shape[0]
-        if self.MIMIC_IMG:
-            assert target_imgs.shape[1:] == self.input_shape[1:]
-            assert source_imgs.shape[0] == target_imgs.shape[0]
-        else:
-            assert target_imgs.shape[1:] == self.bottleneck_shape[1:]
-            assert source_imgs.shape[0] == target_imgs.shape[0]
-
-        start_time = time.time()
-
-        adv_imgs = []
-        print('%d batches in total'
-              % int(np.ceil(len(source_imgs) / self.batch_size)))
-
-        for idx in range(0, len(source_imgs), self.batch_size):
-            print('processing batch %d at %s' % (idx, datetime.datetime.now()))
-            adv_img = self.attack_batch(source_imgs[idx:idx + self.batch_size],
-                                        target_imgs[idx:idx + self.batch_size],
-                                        weights[idx:idx + self.batch_size])
-            adv_imgs.extend(adv_img)
-
-        elapsed_time = time.time() - start_time
-        print('attack cost %f s' % (elapsed_time))
-
-        return np.array(adv_imgs)
-
-    def attack_batch(self, source_imgs, target_imgs, weights):
-
-        """
-        Run the attack on a batch of images and labels.
-        """
-
-        LR = self.learning_rate
-        nb_imgs = source_imgs.shape[0]
-        mask = [True] * nb_imgs + [False] * (self.batch_size - nb_imgs)
-        mask = np.array(mask, dtype=np.bool)
-
-        source_imgs = np.array(source_imgs)
-        target_imgs = np.array(target_imgs)
-
-        # convert to tanh-space
-        simg_tanh = self.preprocess_arctanh(source_imgs)
-        if self.MIMIC_IMG:
-            timg_tanh = self.preprocess_arctanh(target_imgs)
-        else:
-            timg_tanh = target_imgs
-
-        CONST = np.ones(self.batch_size) * self.initial_const
-
-        self.sess.run(self.init)
-        simg_tanh_batch = np.zeros(self.input_shape)
-        if self.MIMIC_IMG:
-            timg_tanh_batch = np.zeros(self.input_shape)
-        else:
-            timg_tanh_batch = np.zeros(self.bottleneck_shape)
-        weights_batch = np.zeros(self.bottleneck_shape)
-        simg_tanh_batch[:nb_imgs] = simg_tanh[:nb_imgs]
-        timg_tanh_batch[:nb_imgs] = timg_tanh[:nb_imgs]
-        weights_batch[:nb_imgs] = weights[:nb_imgs]
-        modifier_batch = np.ones(self.input_shape) * 1e-6
-
-        # set the variables so that we don't have to send them over again
-        if self.MIMIC_IMG:
-            self.sess.run(self.setup,
-                          {self.assign_timg_tanh: timg_tanh_batch,
-                           self.assign_simg_tanh: simg_tanh_batch,
-                           self.assign_const: CONST,
-                           self.assign_mask: mask,
-                           self.assign_weights: weights_batch,
-                           self.assign_modifier: modifier_batch})
-        else:
-            # if directly mimicking a vector, use assign_bottleneck_t_raw
-            # in setup
-            self.sess.run(self.setup,
-                          {self.assign_bottleneck_t_raw: timg_tanh_batch,
-                           self.assign_simg_tanh: simg_tanh_batch,
-                           self.assign_const: CONST,
-                           self.assign_mask: mask,
-                           self.assign_weights: weights_batch,
-                           self.assign_modifier: modifier_batch})
-
-        best_bottlesim = [0] * nb_imgs if self.maximize else [np.inf] * nb_imgs
-        best_adv = np.zeros_like(source_imgs)
-
-        if self.verbose == 1:
-            loss_sum = float(self.sess.run(self.loss_sum))
-            dist_sum = float(self.sess.run(self.dist_sum))
-            thresh_over = (dist_sum / self.batch_size / self.l_threshold * 100)
-            dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
-            bottlesim_sum = self.sess.run(self.bottlesim_sum)
-            print('START: Total loss: %.4E; perturb: %.6f (%.2f%% over, raw: %.6f); sim: %f'
-                  % (Decimal(loss_sum),
-                     dist_sum,
-                     thresh_over,
-                     dist_raw_sum,
-                     bottlesim_sum / nb_imgs))
-
-        try:
-            total_distance = [0] * nb_imgs
-
-            if self.limit_dist:
-                dist_raw_list, bottlesim_list, aimg_input_list = self.sess.run(
-                    [self.dist_raw,
-                     self.bottlesim,
-                     self.aimg_input])
-                for e, (dist_raw, bottlesim, aimg_input) in enumerate(
-                        zip(dist_raw_list, bottlesim_list, aimg_input_list)):
-                    if e >= nb_imgs:
-                        break
-                    total_distance[e] = bottlesim
-
-            for iteration in range(self.MAX_ITERATIONS):
-
-                self.sess.run([self.train], feed_dict={self.learning_rate_holder: LR})
-
-                dist_raw_list, bottlesim_list, aimg_input_list = self.sess.run(
-                    [self.dist_raw,
-                     self.bottlesim,
-                     self.aimg_input])
-                for e, (dist_raw, bottlesim, aimg_input) in enumerate(
-                        zip(dist_raw_list, bottlesim_list, aimg_input_list)):
-                    if e >= nb_imgs:
-                        break
-                    if (bottlesim < best_bottlesim[e] and bottlesim > total_distance[e] * 0.1 and (
-                            not self.maximize)) or (
-                            bottlesim > best_bottlesim[e] and self.maximize):
-                        best_bottlesim[e] = bottlesim
-                        best_adv[e] = aimg_input
-
-                if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0:
-                    LR = LR / 2
-                    print("Learning Rate: ", LR)
-
-                if iteration % (self.MAX_ITERATIONS // 10) == 0:
-                    if self.verbose == 1:
-                        loss_sum = float(self.sess.run(self.loss_sum))
-                        dist_sum = float(self.sess.run(self.dist_sum))
-                        thresh_over = (dist_sum /
-                                       self.batch_size /
-                                       self.l_threshold *
-                                       100)
-                        dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
-                        bottlesim_sum = self.sess.run(self.bottlesim_sum)
-                        print('ITER %4d: Total loss: %.4E; perturb: %.6f (%.2f%% over, raw: %.6f); sim: %f'
-                              % (iteration,
-                                 Decimal(loss_sum),
-                                 dist_sum,
-                                 thresh_over,
-                                 dist_raw_sum,
-                                 bottlesim_sum / nb_imgs))
-        except KeyboardInterrupt:
-            pass
-
-        if self.verbose == 1:
-            loss_sum = float(self.sess.run(self.loss_sum))
-            dist_sum = float(self.sess.run(self.dist_sum))
-            thresh_over = (dist_sum / self.batch_size / self.l_threshold * 100)
-            dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
-            bottlesim_sum = float(self.sess.run(self.bottlesim_sum))
-            print('END:       Total loss: %.4E; perturb: %.6f (%.2f%% over, raw: %.6f); sim: %f'
-                  % (Decimal(loss_sum),
-                     dist_sum,
-                     thresh_over,
-                     dist_raw_sum,
-                     bottlesim_sum / nb_imgs))
-
-        best_adv = self.clipping(best_adv[:nb_imgs])
-
-        return best_adv

fawkes_dev/eval_cloak.py

@@ -1,25 +1,18 @@
+import argparse
+import os
 import sys
 
-sys.path.append("/home/shansixioing/tools/")
-sys.path.append("/home/shansixioing/cloak/")
-
-import argparse
-from tensorflow import set_random_seed
-from utils import init_gpu, load_extractor, load_victim_model, dump_dictionary_as_json
-import os
 import numpy as np
+
+sys.path.append("/home/shansixioing/fawkes/fawkes")
+from utils import extract_faces, get_dataset_path, init_gpu, load_extractor, load_victim_model
+
 import random
-import pickle
+import glob
-import re
 from keras.preprocessing import image
 from keras.utils import to_categorical
 from keras.applications.vgg16 import preprocess_input
 
-# import locale
-#
-# loc = locale.getlocale()
-# locale.setlocale(locale.LC_ALL, loc)
-
 
 def select_samples(data_dir):
     all_data_path = []
@@ -27,43 +20,49 @@ def select_samples(data_dir):
         cls_dir = os.path.join(data_dir, cls)
         for data_path in os.listdir(cls_dir):
             all_data_path.append(os.path.join(cls_dir, data_path))
-
     return all_data_path
 
 
-def generator_wrap(cloak_data, n_classes, test=False, validation_split=0.1):
+def generator_wrap(protect_images, test=False, validation_split=0.1):
-    if test:
+    train_data_dir, test_data_dir, num_classes, num_images = get_dataset_path(args.dataset)
-        all_data_path = select_samples(cloak_data.test_data_dir)
-    else:
-        all_data_path = select_samples(cloak_data.train_data_dir)
-    split = int(len(cloak_data.cloaked_protect_train_X) * (1 - validation_split))
-    cloaked_train_X = cloak_data.cloaked_protect_train_X[:split]
-    np.random.seed(12345)
 
-    # all_vals = list(cloak_data.path2idx.items())
+    idx = 0
+    path2class = {}
+    path2imgs_list = {}
+
+    for target_path in sorted(glob.glob(train_data_dir + "/*")):
+        path2class[target_path] = idx
+        path2imgs_list[target_path] = glob.glob(os.path.join(target_path, "*"))
+        idx += 1
+        if idx >= args.num_classes:
+            break
+
+    path2class["protected"] = idx
+
+    np.random.seed(12345)
 
     while True:
         batch_X = []
         batch_Y = []
-        cur_batch_path = np.random.choice(all_data_path, args.batch_size)
+        cur_batch_path = np.random.choice(list(path2class.keys()), args.batch_size)
         for p in cur_batch_path:
-            # p = p.encode("utf-8").decode("ascii", 'ignore')
+            cur_y = path2class[p]
-            cur_y = cloak_data.path2idx[p]
+            if test and p == 'protected':
-            # protect class and sybil class do not need to appear in test dataset
-            if test and (re.search(cloak_data.protect_class, p)):
                 continue
             # protect class images in train dataset
-            elif p in cloak_data.protect_class_path:
+            elif p == 'protected':
-                cur_x = random.choice(cloaked_train_X)
+                cur_x = random.choice(protect_images)
             else:
-                im = image.load_img(p, target_size=cloak_data.img_shape)
+                cur_path = random.choice(path2imgs_list[p])
-                im = image.img_to_array(im)
+                im = image.load_img(cur_path, target_size=(224, 224))
-                cur_x = preprocess_input(im)
+                cur_x = image.img_to_array(im)
+
+            cur_x = preprocess_input(cur_x)
             batch_X.append(cur_x)
             batch_Y.append(cur_y)
 
         batch_X = np.array(batch_X)
-        batch_Y = to_categorical(np.array(batch_Y), num_classes=n_classes)
+        batch_Y = to_categorical(np.array(batch_Y), num_classes=args.num_classes + 1)
 
         yield batch_X, batch_Y
 
@@ -87,51 +86,59 @@ def eval_cloaked_test_data(cloak_data, n_classes, validation_split=0.1):
 
 def main():
     init_gpu(args.gpu)
-
+    #
-    if args.dataset == 'pubfig':
+    # if args.dataset == 'pubfig':
-        N_CLASSES = 65
+    #     N_CLASSES = 65
-        CLOAK_DIR = args.cloak_data
+    #     CLOAK_DIR = args.cloak_data
-    elif args.dataset == 'scrub':
+    # elif args.dataset == 'scrub':
-        N_CLASSES = 530
+    #     N_CLASSES = 530
-        CLOAK_DIR = args.cloak_data
+    #     CLOAK_DIR = args.cloak_data
-    else:
+    # else:
-        raise ValueError
+    #     raise ValueError
-
-    CLOAK_DIR = os.path.join("../results", CLOAK_DIR)
-    RES = pickle.load(open(os.path.join(CLOAK_DIR, "cloak_data.p"), 'rb'))
 
     print("Build attacker's model")
-    cloak_data = RES['cloak_data']
+
-    EVAL_RES = {}
+    image_paths = glob.glob(os.path.join(args.directory, "*"))
-    train_generator = generator_wrap(cloak_data, n_classes=N_CLASSES,
+    original_image_paths = sorted([path for path in image_paths if "_cloaked" not in path.split("/")[-1]])
+
+    protect_image_paths = sorted([path for path in image_paths if "_cloaked" in path.split("/")[-1]])
+
+    original_imgs = np.array([extract_faces(image.img_to_array(image.load_img(cur_path))) for cur_path in
+                     original_image_paths[:150]])
+    original_y = to_categorical([args.num_classes] * len(original_imgs), num_classes=args.num_classes + 1)
+
+    protect_imgs = [extract_faces(image.img_to_array(image.load_img(cur_path))) for cur_path in
+                    protect_image_paths]
+
+    train_generator = generator_wrap(protect_imgs,
                                      validation_split=args.validation_split)
-    test_generator = generator_wrap(cloak_data, test=True, n_classes=N_CLASSES,
+    test_generator = generator_wrap(protect_imgs, test=True,
                                     validation_split=args.validation_split)
 
-    EVAL_RES['transfer_model'] = args.transfer_model
-
     base_model = load_extractor(args.transfer_model)
-    model = load_victim_model(teacher_model=base_model, number_classes=N_CLASSES)
+    model = load_victim_model(teacher_model=base_model, number_classes=args.num_classes + 1)
 
-    original_X, original_Y = eval_uncloaked_test_data(cloak_data, N_CLASSES)
+    # cloaked_test_X, cloaked_test_Y = eval_cloaked_test_data(cloak_data, args.num_classes,
-    cloaked_test_X, cloaked_test_Y = eval_cloaked_test_data(cloak_data, N_CLASSES,
+    #                                                         validation_split=args.validation_split)
-                                                            validation_split=args.validation_split)
 
-    try:
+    # try:
-        model.fit_generator(train_generator, steps_per_epoch=cloak_data.number_samples // 32,
+    train_data_dir, test_data_dir, num_classes, num_images = get_dataset_path(args.dataset)
-                            validation_data=(original_X, original_Y), epochs=args.n_epochs, verbose=2,
+    model.fit_generator(train_generator, steps_per_epoch=num_images // 32,
-                            use_multiprocessing=False, workers=1)
+                        validation_data=(original_imgs, original_y),
-    except KeyboardInterrupt:
+                        epochs=args.n_epochs,
-        pass
+                        verbose=1,
+                        use_multiprocessing=True, workers=5)
+    # except KeyboardInterrupt:
+    #     pass
 
-    _, acc_original = model.evaluate(original_X, original_Y, verbose=0)
+    _, acc_original = model.evaluate(original_imgs, original_y, verbose=0)
     print("Accuracy on uncloaked/original images TEST: {:.4f}".format(acc_original))
-    EVAL_RES['acc_original'] = acc_original
+    # EVAL_RES['acc_original'] = acc_original
 
     _, other_acc = model.evaluate_generator(test_generator, verbose=0, steps=50)
     print("Accuracy on other classes {:.4f}".format(other_acc))
-    EVAL_RES['other_acc'] = other_acc
+    # EVAL_RES['other_acc'] = other_acc
-    dump_dictionary_as_json(EVAL_RES, os.path.join(CLOAK_DIR, "eval_seed{}.json".format(args.seed_idx)))
+    # dump_dictionary_as_json(EVAL_RES, os.path.join(CLOAK_DIR, "eval_seed{}.json".format(args.seed_idx)))
 
 
 def parse_arguments(argv):
@@ -139,16 +146,21 @@ def parse_arguments(argv):
 
     parser.add_argument('--gpu', type=str,
                         help='GPU id', default='0')
+
     parser.add_argument('--dataset', type=str,
                         help='name of dataset', default='scrub')
-    parser.add_argument('--cloak_data', type=str,
+    parser.add_argument('--num_classes', type=int,
+                        help='name of dataset', default=520)
+
+    parser.add_argument('--directory', '-d', type=str,
                         help='name of the cloak result directory',
-                        default='scrub_webface_dense_robust_extract_protectPatrick_Dempsey')
+                        default='img/')
+
     parser.add_argument('--transfer_model', type=str,
-                        help='the feature extractor used for tracker model training. It can be the same or not same as the user\'s', default='vggface2_inception_extract')
+                        help='the feature extractor used for tracker model training. ', default='low_extract')
     parser.add_argument('--batch_size', type=int, default=32)
     parser.add_argument('--validation_split', type=float, default=0.1)
-    parser.add_argument('--n_epochs', type=int, default=5)
+    parser.add_argument('--n_epochs', type=int, default=3)
     return parser.parse_args(argv)
 
 

fawkes_dev/prepare_feature_extractor.py

@@ -1,4 +1,5 @@
 import argparse
+import glob
 import os
 import pickle
 import random
@@ -7,8 +8,9 @@ import sys
 import numpy as np
 from keras.applications.vgg16 import preprocess_input
 from keras.preprocessing import image
-from utils import load_extractor, get_dataset_path
+sys.path.append("../fawkes")
-
+# from utils import load_extractor
+import keras
 
 def load_sample_dir(path, sample=10):
     x_ls = []
@@ -30,21 +32,26 @@ def normalize(x):
 
 
 def main():
-    extractor = load_extractor(args.feature_extractor)
+    extractor = keras.models.load_model(args.feature_extractor)
+
     path2emb = {}
-    for target_dataset in args.candidate_datasets:
+    model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
-        target_dataset_path, _, _, _ = get_dataset_path(target_dataset)
+    for path in glob.glob(os.path.join(model_dir, "target_data/*")):
-        for target_class in os.listdir(target_dataset_path):
+        print(path)
-            target_class_path = os.path.join(target_dataset_path, target_class)
+        idx = int(path.split("/")[-1])
-            target_X = load_sample_dir(target_class_path)
+        cur_image_paths = glob.glob(os.path.join(path, "*"))
-            cur_feature = extractor.predict(target_X)
+        imgs = np.array([image.img_to_array(image.load_img(p, target_size=(224, 224))) for p in cur_image_paths])
-            cur_feature = np.mean(cur_feature, axis=0)
+        imgs = preprocess_input(imgs)
-            path2emb[target_class_path] = cur_feature
 
-    for k, v in path2emb.items():
+        cur_feature = extractor.predict(imgs)
-        path2emb[k] = normalize(v)
+        cur_feature = np.mean(cur_feature, axis=0)
+        path2emb[idx] = cur_feature
+
+    model_path = os.path.join(model_dir, "{}_extract.h5".format(args.feature_extractor_name))
+    emb_path = os.path.join(model_dir, "{}_emb.p".format(args.feature_extractor_name))
+    extractor.save(model_path)
+    pickle.dump(path2emb, open(emb_path, "wb"))
 
-    pickle.dump(path2emb, open("../feature_extractors/embeddings/{}_emb.p".format(args.feature_extractor), "wb"))
 
 
 def parse_arguments(argv):
@@ -54,8 +61,12 @@ def parse_arguments(argv):
     parser.add_argument('--candidate-datasets', nargs='+',
                         help='path candidate datasets')
     parser.add_argument('--feature-extractor', type=str,
+                        help="path of the feature extractor used for optimization",
+                        default="/home/shansixioing/fawkes/feature_extractors/high2_extract.h5")
+    parser.add_argument('--feature-extractor-name', type=str,
                         help="name of the feature extractor used for optimization",
-                        default="webface_dense_robust_extract")
+                        default="high2")
+
     return parser.parse_args(argv)
 
 

fawkes_dev/protection.py

@@ -1,95 +0,0 @@
-import argparse
-import os
-import pickle
-import random
-import sys
-
-import numpy as np
-from differentiator import FawkesMaskGeneration
-from tensorflow import set_random_seed
-from utils import load_extractor, CloakData, init_gpu
-
-random.seed(12243)
-np.random.seed(122412)
-set_random_seed(12242)
-
-NUM_IMG_PROTECTED = 400  # Number of images used to optimize the target class
-BATCH_SIZE = 32
-
-MAX_ITER = 1000
-
-
-def diff_protected_data(sess, feature_extractors_ls, image_X, number_protect, target_X=None, th=0.01):
-    image_X = image_X[:number_protect]
-    differentiator = FawkesMaskGeneration(sess, feature_extractors_ls,
-                                          batch_size=BATCH_SIZE,
-                                          mimic_img=True,
-                                          intensity_range='imagenet',
-                                          initial_const=args.sd,
-                                          learning_rate=args.lr,
-                                          max_iterations=MAX_ITER,
-                                          l_threshold=th,
-                                          verbose=1, maximize=False, keep_final=False, image_shape=image_X.shape[1:])
-
-    if len(target_X) < len(image_X):
-        target_X = np.concatenate([target_X, target_X, target_X])
-    target_X = target_X[:len(image_X)]
-    cloaked_image_X = differentiator.attack(image_X, target_X)
-    return cloaked_image_X
-
-
-def perform_defense():
-    RES = {}
-    sess = init_gpu(args.gpu)
-
-    FEATURE_EXTRACTORS = [args.feature_extractor]
-    RES_DIR = '../results/'
-
-    RES['num_img_protected'] = NUM_IMG_PROTECTED
-    RES['extractors'] = FEATURE_EXTRACTORS
-    num_protect = NUM_IMG_PROTECTED
-
-    print("Loading {} for optimization".format(args.feature_extractor))
-    feature_extractors_ls = [load_extractor(name) for name in FEATURE_EXTRACTORS]
-    protect_class = args.protect_class
-
-    cloak_data = CloakData(args.dataset, protect_class=protect_class)
-    RES_FILE_NAME = "{}_{}_protect{}".format(args.dataset, args.feature_extractor, cloak_data.protect_class)
-    RES_FILE_NAME = os.path.join(RES_DIR, RES_FILE_NAME)
-    print("Protect Class: ", cloak_data.protect_class)
-
-    cloak_data.target_path, cloak_data.target_data = cloak_data.select_target_label(feature_extractors_ls,
-                                                                                    FEATURE_EXTRACTORS)
-
-    os.makedirs(RES_DIR, exist_ok=True)
-    os.makedirs(RES_FILE_NAME, exist_ok=True)
-
-    cloak_image_X = diff_protected_data(sess, feature_extractors_ls, cloak_data.protect_train_X,
-                                        number_protect=num_protect,
-                                        target_X=cloak_data.target_data, th=args.th)
-
-    cloak_data.cloaked_protect_train_X = cloak_image_X
-    RES['cloak_data'] = cloak_data
-    pickle.dump(RES, open(os.path.join(RES_FILE_NAME, 'cloak_data.p'), "wb"))
-
-
-def parse_arguments(argv):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--gpu', type=str,
-                        help='GPU id', default='0')
-    parser.add_argument('--dataset', type=str,
-                        help='name of dataset', default='scrub')
-    parser.add_argument('--feature-extractor', type=str,
-                        help="name of the feature extractor used for optimization",
-                        default="webface_dense_robust_extract")
-    parser.add_argument('--th', type=float, default=0.007)
-    parser.add_argument('--sd', type=int, default=1e9)
-    parser.add_argument('--protect_class', type=str, default=None)
-    parser.add_argument('--lr', type=float, default=1)
-
-    return parser.parse_args(argv)
-
-
-if __name__ == '__main__':
-    args = parse_arguments(sys.argv[1:])
-    perform_defense()

fawkes_dev/utils.py

@@ -1,373 +0,0 @@
-import json
-import os
-import pickle
-import random
-
-import keras
-import keras.backend as K
-import numpy as np
-import tensorflow as tf
-from keras.applications.vgg16 import preprocess_input
-from keras.layers import Dense, Activation
-from keras.models import Model
-from keras.preprocessing import image
-from keras.utils import to_categorical
-from sklearn.metrics import pairwise_distances
-# from keras.utils import get_file
-
-def clip_img(X, preprocessing='raw'):
-    X = reverse_preprocess(X, preprocessing)
-    X = np.clip(X, 0.0, 255.0)
-    X = preprocess(X, preprocessing)
-    return X
-
-
-def dump_dictionary_as_json(dict, outfile):
-    j = json.dumps(dict)
-    with open(outfile, "wb") as f:
-        f.write(j.encode())
-
-
-def fix_gpu_memory(mem_fraction=1):
-    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_fraction)
-    tf_config = tf.ConfigProto(gpu_options=gpu_options)
-    tf_config.gpu_options.allow_growth = True
-    tf_config.log_device_placement = False
-    init_op = tf.global_variables_initializer()
-    sess = tf.Session(config=tf_config)
-    sess.run(init_op)
-    K.set_session(sess)
-    return sess
-
-
-def load_victim_model(number_classes, teacher_model=None, end2end=False):
-    for l in teacher_model.layers:
-        l.trainable = end2end
-    x = teacher_model.layers[-1].output
-
-    x = Dense(number_classes)(x)
-    x = Activation('softmax', name="act")(x)
-    model = Model(teacher_model.input, x)
-    opt = keras.optimizers.Adadelta()
-    model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
-    return model
-
-
-def init_gpu(gpu_index, force=False):
-    if isinstance(gpu_index, list):
-        gpu_num = ','.join([str(i) for i in gpu_index])
-    else:
-        gpu_num = str(gpu_index)
-    if "CUDA_VISIBLE_DEVICES" in os.environ and os.environ["CUDA_VISIBLE_DEVICES"] and not force:
-        print('GPU already initiated')
-        return
-    os.environ["CUDA_VISIBLE_DEVICES"] = gpu_num
-    sess = fix_gpu_memory()
-    return sess
-
-
-def preprocess(X, method):
-    assert method in {'raw', 'imagenet', 'inception', 'mnist'}
-
-    if method is 'raw':
-        pass
-    elif method is 'imagenet':
-        X = imagenet_preprocessing(X)
-    else:
-        raise Exception('unknown method %s' % method)
-
-    return X
-
-
-def reverse_preprocess(X, method):
-    assert method in {'raw', 'imagenet', 'inception', 'mnist'}
-
-    if method is 'raw':
-        pass
-    elif method is 'imagenet':
-        X = imagenet_reverse_preprocessing(X)
-    else:
-        raise Exception('unknown method %s' % method)
-
-    return X
-
-
-def imagenet_preprocessing(x, data_format=None):
-    if data_format is None:
-        data_format = K.image_data_format()
-    assert data_format in ('channels_last', 'channels_first')
-
-    x = np.array(x)
-    if data_format == 'channels_first':
-        # 'RGB'->'BGR'
-        if x.ndim == 3:
-            x = x[::-1, ...]
-        else:
-            x = x[:, ::-1, ...]
-    else:
-        # 'RGB'->'BGR'
-        x = x[..., ::-1]
-
-    mean = [103.939, 116.779, 123.68]
-    std = None
-
-    # Zero-center by mean pixel
-    if data_format == 'channels_first':
-        if x.ndim == 3:
-            x[0, :, :] -= mean[0]
-            x[1, :, :] -= mean[1]
-            x[2, :, :] -= mean[2]
-            if std is not None:
-                x[0, :, :] /= std[0]
-                x[1, :, :] /= std[1]
-                x[2, :, :] /= std[2]
-        else:
-            x[:, 0, :, :] -= mean[0]
-            x[:, 1, :, :] -= mean[1]
-            x[:, 2, :, :] -= mean[2]
-            if std is not None:
-                x[:, 0, :, :] /= std[0]
-                x[:, 1, :, :] /= std[1]
-                x[:, 2, :, :] /= std[2]
-    else:
-        x[..., 0] -= mean[0]
-        x[..., 1] -= mean[1]
-        x[..., 2] -= mean[2]
-        if std is not None:
-            x[..., 0] /= std[0]
-            x[..., 1] /= std[1]
-            x[..., 2] /= std[2]
-
-    return x
-
-
-def imagenet_reverse_preprocessing(x, data_format=None):
-    import keras.backend as K
-    x = np.array(x)
-    if data_format is None:
-        data_format = K.image_data_format()
-    assert data_format in ('channels_last', 'channels_first')
-
-    if data_format == 'channels_first':
-        if x.ndim == 3:
-            # Zero-center by mean pixel
-            x[0, :, :] += 103.939
-            x[1, :, :] += 116.779
-            x[2, :, :] += 123.68
-            # 'BGR'->'RGB'
-            x = x[::-1, :, :]
-        else:
-            x[:, 0, :, :] += 103.939
-            x[:, 1, :, :] += 116.779
-            x[:, 2, :, :] += 123.68
-            x = x[:, ::-1, :, :]
-    else:
-        # Zero-center by mean pixel
-        x[..., 0] += 103.939
-        x[..., 1] += 116.779
-        x[..., 2] += 123.68
-        # 'BGR'->'RGB'
-        x = x[..., ::-1]
-    return x
-
-
-
-def build_bottleneck_model(model, cut_off):
-    bottleneck_model = Model(model.input, model.get_layer(cut_off).output)
-    bottleneck_model.compile(loss='categorical_crossentropy',
-                             optimizer='adam',
-                             metrics=['accuracy'])
-    return bottleneck_model
-
-
-def load_extractor(name):
-    model = keras.models.load_model("../feature_extractors/{}.h5".format(name))
-    if hasattr(model.layers[-1], "activation") and model.layers[-1].activation == "softmax":
-        raise Exception(
-            "Given extractor's last layer is softmax, need to remove the top layers to make it into a feature extractor")
-    # if "extract" in name.split("/")[-1]:
-    #     pass
-    # else:
-    #     print("Convert a model to a feature extractor")
-    #     model = build_bottleneck_model(model, model.layers[layer_idx].name)
-    #     model.save(name + "extract")
-    #     model = keras.models.load_model(name + "extract")
-    return model
-
-
-def get_dataset_path(dataset):
-    if not os.path.exists("config.json"):
-        raise Exception("Please config the datasets before running protection code. See more in README and config.py.")
-
-    config = json.load(open("config.json", 'r'))
-    if dataset not in config:
-        raise Exception(
-            "Dataset {} does not exist, please download to data/ and add the path to this function... Abort".format(
-                dataset))
-    return config[dataset]['train_dir'], config[dataset]['test_dir'], config[dataset]['num_classes'], config[dataset][
-        'num_images']
-
-
-def normalize(x):
-    return x / np.linalg.norm(x, axis=1, keepdims=True)
-
-
-class CloakData(object):
-    def __init__(self, dataset, img_shape=(224, 224), protect_class=None):
-        self.dataset = dataset
-        self.img_shape = img_shape
-
-        self.train_data_dir, self.test_data_dir, self.number_classes, self.number_samples = get_dataset_path(dataset)
-        self.all_labels = sorted(list(os.listdir(self.train_data_dir)))
-        if protect_class:
-            self.protect_class = protect_class
-        else:
-            self.protect_class = random.choice(self.all_labels)
-
-        self.sybil_class = random.choice([l for l in self.all_labels if l != self.protect_class])
-        self.protect_train_X, self.protect_test_X = self.load_label_data(self.protect_class)
-        self.sybil_train_X, self.sybil_test_X = self.load_label_data(self.sybil_class)
-
-        self.cloaked_protect_train_X = None
-        self.cloaked_sybil_train_X = None
-
-        self.label2path_train, self.label2path_test, self.path2idx = self.build_data_mapping()
-        self.all_training_path = self.get_all_data_path(self.label2path_train)
-        self.all_test_path = self.get_all_data_path(self.label2path_test)
-        self.protect_class_path = self.get_class_image_files(os.path.join(self.train_data_dir, self.protect_class))
-        self.sybil_class_path = self.get_class_image_files(os.path.join(self.train_data_dir, self.sybil_class))
-
-        print("Find {} protect images".format(len(self.protect_class_path)))
-
-    def get_class_image_files(self, path):
-        return [os.path.join(path, f) for f in os.listdir(path)]
-
-    def extractor_ls_predict(self, feature_extractors_ls, X):
-        feature_ls = []
-        for extractor in feature_extractors_ls:
-            cur_features = extractor.predict(X)
-            feature_ls.append(cur_features)
-        concated_feature_ls = np.concatenate(feature_ls, axis=1)
-        concated_feature_ls = normalize(concated_feature_ls)
-        return concated_feature_ls
-
-    def load_embeddings(self, feature_extractors_names):
-        dictionaries = []
-        for extractor_name in feature_extractors_names:
-            path2emb = pickle.load(open("../feature_extractors/embeddings/{}_emb_norm.p".format(extractor_name), "rb"))
-            dictionaries.append(path2emb)
-
-        merge_dict = {}
-        for k in dictionaries[0].keys():
-            cur_emb = [dic[k] for dic in dictionaries]
-            merge_dict[k] = np.concatenate(cur_emb)
-        return merge_dict
-
-    def select_target_label(self, feature_extractors_ls, feature_extractors_names, metric='l2'):
-        original_feature_x = self.extractor_ls_predict(feature_extractors_ls, self.protect_train_X)
-
-        path2emb = self.load_embeddings(feature_extractors_names)
-        items = list(path2emb.items())
-        paths = [p[0] for p in items]
-        embs = [p[1] for p in items]
-        embs = np.array(embs)
-
-        pair_dist = pairwise_distances(original_feature_x, embs, metric)
-        max_sum = np.min(pair_dist, axis=0)
-        sorted_idx = np.argsort(max_sum)[::-1]
-
-        highest_num = 0
-        paired_target_X = None
-        final_target_class_path = None
-        for idx in sorted_idx[:5]:
-            target_class_path = paths[idx]
-            cur_target_X = self.load_dir(target_class_path)
-            cur_target_X = np.concatenate([cur_target_X, cur_target_X, cur_target_X])
-            cur_tot_sum, cur_paired_target_X = self.calculate_dist_score(self.protect_train_X, cur_target_X,
-                                                                         feature_extractors_ls,
-                                                                         metric=metric)
-            if cur_tot_sum > highest_num:
-                highest_num = cur_tot_sum
-                paired_target_X = cur_paired_target_X
-                final_target_class_path = target_class_path
-
-        np.random.shuffle(paired_target_X)
-        return final_target_class_path, paired_target_X
-
-    def calculate_dist_score(self, a, b, feature_extractors_ls, metric='l2'):
-        features1 = self.extractor_ls_predict(feature_extractors_ls, a)
-        features2 = self.extractor_ls_predict(feature_extractors_ls, b)
-
-        pair_cos = pairwise_distances(features1, features2, metric)
-        max_sum = np.min(pair_cos, axis=0)
-        max_sum_arg = np.argsort(max_sum)[::-1]
-        max_sum_arg = max_sum_arg[:len(a)]
-        max_sum = [max_sum[i] for i in max_sum_arg]
-        paired_target_X = [b[j] for j in max_sum_arg]
-        paired_target_X = np.array(paired_target_X)
-        return np.min(max_sum), paired_target_X
-
-    def get_all_data_path(self, label2path):
-        all_paths = []
-        for k, v in label2path.items():
-            cur_all_paths = [os.path.join(k, cur_p) for cur_p in v]
-            all_paths.extend(cur_all_paths)
-        return all_paths
-
-    def load_label_data(self, label):
-        train_label_path = os.path.join(self.train_data_dir, label)
-        test_label_path = os.path.join(self.test_data_dir, label)
-        train_X = self.load_dir(train_label_path)
-        test_X = self.load_dir(test_label_path)
-        return train_X, test_X
-
-    def load_dir(self, path):
-        assert os.path.exists(path)
-        x_ls = []
-        for file in os.listdir(path):
-            cur_path = os.path.join(path, file)
-            im = image.load_img(cur_path, target_size=self.img_shape)
-            im = image.img_to_array(im)
-            x_ls.append(im)
-        raw_x = np.array(x_ls)
-        return preprocess_input(raw_x)
-
-    def build_data_mapping(self):
-        label2path_train = {}
-        label2path_test = {}
-        idx = 0
-        path2idx = {}
-        for label_name in self.all_labels:
-            full_path_train = os.path.join(self.train_data_dir, label_name)
-            full_path_test = os.path.join(self.test_data_dir, label_name)
-            label2path_train[full_path_train] = list(os.listdir(full_path_train))
-            label2path_test[full_path_test] = list(os.listdir(full_path_test))
-            for img_file in os.listdir(full_path_train):
-                path2idx[os.path.join(full_path_train, img_file)] = idx
-            for img_file in os.listdir(full_path_test):
-                path2idx[os.path.join(full_path_test, img_file)] = idx
-            idx += 1
-        return label2path_train, label2path_test, path2idx
-
-    def generate_data_post_cloak(self, sybil=False):
-        assert self.cloaked_protect_train_X is not None
-        while True:
-            batch_X = []
-            batch_Y = []
-            cur_batch_path = random.sample(self.all_training_path, 32)
-            for p in cur_batch_path:
-                cur_y = self.path2idx[p]
-                if p in self.protect_class_path:
-                    cur_x = random.choice(self.cloaked_protect_train_X)
-                elif sybil and (p in self.sybil_class):
-                    cur_x = random.choice(self.cloaked_sybil_train_X)
-                else:
-                    im = image.load_img(p, target_size=self.img_shape)
-                    im = image.img_to_array(im)
-                    cur_x = preprocess_input(im)
-                batch_X.append(cur_x)
-                batch_Y.append(cur_y)
-            batch_X = np.array(batch_X)
-            batch_Y = to_categorical(np.array(batch_Y), num_classes=self.number_classes)
-            yield batch_X, batch_Y