ResearchPapers/fawkes
ResearchPapers/fawkes
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Shawn-Shan 2020-07-07 11:15:47 -05:00
parent e9f1a50653
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2
.gitignore vendored
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@ -12,6 +12,8 @@ fawkes.egg-info
.coverage
.cache
.pytest_cache
fawkes_dev/api_key.txt
fawkes_dev/protect_personId.txt
# developer environments
.idea

24
build/lib/fawkes/__init__.py
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# -*- coding: utf-8 -*-
# @Date : 2020-07-01
# @Author : Shawn Shan (shansixiong@cs.uchicago.edu)
# @Link : https://www.shawnshan.com/
__version__ = '0.0.2'
from .differentiator import FawkesMaskGeneration
from .utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, \
Faces
from .protection import main
import logging
import sys
import os
logging.getLogger('tensorflow').disabled = True
__all__ = (
'__version__',
'FawkesMaskGeneration', 'load_extractor',
'init_gpu',
'select_target_label', 'dump_image', 'reverse_process_cloaked', 'Faces', 'main'
)

449
build/lib/fawkes/differentiator.py
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#!/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, faces=None):
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.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))
# 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
# dist = tf.square(aimg_raw_split - simg_raw_split)
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 = self.dist_raw
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
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 = self.timg_input
cur_simg_input = self.simg_input
self.bottleneck_t = calculate_direction(bottleneck_model, cur_timg_input, cur_simg_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_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)))
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, 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 = 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 = [True] * self.batch_size
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
temp_images = []
# 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))
finished_idx = set()
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])
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 (
not self.maximize)) or (
bottlesim > best_bottlesim[e] and self.maximize):
best_bottlesim[e] = bottlesim
best_adv[e] = aimg_input
# if iteration > 20 and (dist_raw >= self.l_threshold or iteration == self.MAX_ITERATIONS - 1):
# 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 // 5) == 0:
if self.verbose == 1:
dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
bottlesim_sum = self.sess.run(self.bottlesim_sum)
print('ITER %4d perturb: %.5f; sim: %f'
% (iteration, dist_raw_sum / nb_imgs, bottlesim_sum / nb_imgs))
# protected_images = aimg_input_list
#
# orginal_images = np.copy(self.faces.cropped_faces)
# 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))
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 (raw: %.6f); sim: %f'
% (Decimal(loss_sum),
dist_sum,
dist_raw_sum,
bottlesim_sum / nb_imgs))
best_adv = self.clipping(best_adv[:nb_imgs])
return best_adv

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build/lib/fawkes/protection.py
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# from __future__ import absolute_import
# from __future__ import division
# from __future__ import print_function
import argparse
import glob
import os
import random
import sys
import numpy as np
from .differentiator import FawkesMaskGeneration
from .utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, \
Faces
random.seed(12243)
np.random.seed(122412)
BATCH_SIZE = 32
def generate_cloak_images(sess, feature_extractors, image_X, target_emb=None, th=0.01, faces=None, sd=1e9, lr=2,
max_step=500):
batch_size = BATCH_SIZE if len(image_X) > BATCH_SIZE else len(image_X)
differentiator = FawkesMaskGeneration(sess, feature_extractors,
batch_size=batch_size,
mimic_img=True,
intensity_range='imagenet',
initial_const=sd,
learning_rate=lr,
max_iterations=max_step,
l_threshold=th,
verbose=1, maximize=False, keep_final=False, image_shape=image_X.shape[1:],
faces=faces)
cloaked_image_X = differentiator.attack(image_X, target_emb)
return cloaked_image_X
def check_imgs(imgs):
if np.max(imgs) <= 1 and np.min(imgs) >= 0:
imgs = imgs * 255.0
elif np.max(imgs) <= 255 and np.min(imgs) >= 0:
pass
else:
raise Exception("Image values ")
return imgs
def main(*argv):
if not argv:
argv = list(sys.argv)
# attach SIGPIPE handler to properly handle broken pipe
try: # sigpipe not available under windows. just ignore in this case
import signal
signal.signal(signal.SIGPIPE, signal.SIG_DFL)
except Exception as e:
pass
parser = argparse.ArgumentParser()
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='high')
parser.add_argument('--feature-extractor', type=str,
help="name of the feature extractor used for optimization",
default="high_extract")
parser.add_argument('--th', type=float, default=0.01)
parser.add_argument('--max-step', type=int, default=500)
parser.add_argument('--sd', type=int, default=1e9)
parser.add_argument('--lr', type=float, default=2)
parser.add_argument('--separate_target', action='store_true')
parser.add_argument('--format', type=str,
help="final image format",
default="jpg")
args = parser.parse_args(argv[1:])
if args.mode == 'low':
args.feature_extractor = "high_extract"
args.th = 0.003
elif args.mode == 'mid':
args.feature_extractor = "high_extract"
args.th = 0.005
elif args.mode == 'high':
args.feature_extractor = "high_extract"
args.th = 0.007
elif args.mode == 'ultra':
args.feature_extractor = "high_extract"
args.th = 0.01
elif args.mode == 'custom':
pass
else:
raise Exception("mode must be one of 'low', 'mid', 'high', 'ultra', 'custom'")
assert args.format in ['png', 'jpg', 'jpeg']
if args.format == 'jpg':
args.format = 'jpeg'
sess = init_gpu(args.gpu)
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]]
if not image_paths:
print("No images in the directory")
exit(1)
faces = Faces(image_paths, sess)
orginal_images = faces.cropped_faces
orginal_images = np.array(orginal_images)
if args.separate_target:
target_embedding = []
for org_img in orginal_images:
org_img = org_img.reshape([1] + list(org_img.shape))
tar_emb = select_target_label(org_img, feature_extractors_ls, fs_names)
target_embedding.append(tar_emb)
target_embedding = np.concatenate(target_embedding)
else:
target_embedding = select_target_label(orginal_images, feature_extractors_ls, fs_names)
protected_images = generate_cloak_images(sess, feature_extractors_ls, orginal_images,
target_emb=target_embedding, th=args.th, faces=faces, sd=args.sd,
lr=args.lr, max_step=args.max_step)
faces.cloaked_cropped_faces = protected_images
cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(orginal_images)
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.format)
dump_image(p_img, file_name, format=args.format)
if __name__ == '__main__':
main(*sys.argv)

574
build/lib/fawkes/utils.py
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import glob
import gzip
import json
import os
import pickle
import random
import sys
stderr = sys.stderr
sys.stderr = open(os.devnull, 'w')
import keras
sys.stderr = stderr
import keras.backend as K
import numpy as np
import tensorflow as tf
from PIL import Image, ExifTags
# 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 skimage.transform import resize
from sklearn.metrics import pairwise_distances
from .align_face import align, aligner
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 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))
if not self.cropped_faces:
print("No faces detected")
exit(1)
self.cropped_faces = np.array(self.cropped_faces)
self.cropped_faces = preprocess(self.cropped_faces, 'imagenet')
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):
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:
f.write(j.encode())
def fix_gpu_memory(mem_fraction=1):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf_config = None
if tf.test.is_gpu_available():
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 reverse_process_cloaked(x, preprocess='imagenet'):
x = clip_img(x, preprocess)
return reverse_preprocess(x, preprocess)
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_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")
# 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):
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(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(
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)
def dump_image(x, filename, format="png", scale=False):
# img = image.array_to_img(x, scale=scale)
img = image.array_to_img(x)
img.save(filename, format)
return
def load_dir(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=(224, 224))
im = image.img_to_array(im)
x_ls.append(im)
raw_x = np.array(x_ls)
return preprocess(raw_x, 'imagenet')
def load_embeddings(feature_extractors_names):
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
dictionaries = []
for extractor_name in feature_extractors_names:
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 = {}
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 extractor_ls_predict(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 calculate_dist_score(a, b, feature_extractors_ls, metric='l2'):
features1 = extractor_ls_predict(feature_extractors_ls, a)
features2 = 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 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)
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)
max_id = np.argmax(max_sum)
target_data_id = paths[int(max_id)]
image_dir = os.path.join(model_dir, "target_data/{}/*".format(target_data_id))
if not os.path.exists(image_dir):
get_file("{}.h5".format(name), "http://sandlab.cs.uchicago.edu/fawkes/files/target_images".format(name),
cache_dir=model_dir, cache_subdir='')
image_paths = glob.glob(image_dir)
target_images = [image.img_to_array(image.load_img(cur_path)) for cur_path in
image_paths]
target_images = np.array([resize(x, (224, 224)) for x in target_images])
target_images = preprocess(target_images, 'imagenet')
target_images = list(target_images)
while len(target_images) < len(imgs):
target_images += target_images
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
#
# self.protect_X = self.load_label_data(self.protect_directory)
#
# self.cloaked_protect_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))
#
# 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