ResearchPapers/fawkes
ResearchPapers/fawkes
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refactor code

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Shawn-Shan 2020-05-18 15:35:14 -05:00
parent eeffa82598
commit de558e841e
6 changed files with 144 additions and 840 deletions
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0
fawkes/__init__.py Normal file
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4
fawkes/differentiator.py
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@ -9,7 +9,7 @@ import time
from decimal import Decimal
import numpy as np
import tensorflow as tf
from .utils import preprocess, reverse_preprocess
from utils import preprocess, reverse_preprocess
class FawkesMaskGeneration:
@ -391,7 +391,7 @@ class FawkesMaskGeneration:
if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0:
LR = LR / 2
print("Learning Rate: ", LR)
# print out the losses every 10%
if iteration % (self.MAX_ITERATIONS // 10) == 0:
if self.verbose == 1:
loss_sum = float(self.sess.run(self.loss_sum))

617
fawkes/encode_utils.py
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@ -1,617 +0,0 @@
import sys
sys.path.append("/home/shansixioing/tools/")
import gen_utils
import keras, os
from keras.preprocessing import image
from skimage.transform import resize
from sklearn.model_selection import train_test_split
from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D, MaxPooling2D, Dense, Activation, Layer
import keras.backend as K
import random, pickle
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import preprocess_input
from sklearn.metrics import pairwise_distances
from keras.utils import to_categorical
def load_dataset_deepid(full=False, num_classes=1283, preprocess='raw'):
if not full:
X_train, Y_train = gen_utils.load_h5py(["X_train", "Y_train"],
"/mnt/data/sixiongshan/backdoor/data/deepid/deepid_data_training_0.h5")
else:
X_train_0, Y_train_0 = gen_utils.load_h5py(["X_train", "Y_train"],
"/mnt/data/sixiongshan/backdoor/data/deepid/deepid_data_training_0.h5")
X_train_1, Y_train_1 = gen_utils.load_h5py(["X_train", "Y_train"],
"/mnt/data/sixiongshan/backdoor/data/deepid/deepid_data_training_1.h5")
X_train_2, Y_train_2 = gen_utils.load_h5py(["X_train", "Y_train"],
"/mnt/data/sixiongshan/backdoor/data/deepid/deepid_data_training_2.h5")
X_train_3, Y_train_3 = gen_utils.load_h5py(["X_train", "Y_train"],
"/mnt/data/sixiongshan/backdoor/data/deepid/deepid_data_training_3.h5")
X_train = np.concatenate([X_train_0, X_train_1, X_train_2, X_train_3])
Y_train = np.concatenate([Y_train_0, Y_train_1, Y_train_2, Y_train_3])
X_test, Y_test = gen_utils.load_h5py(["X_test", "Y_test"],
"/mnt/data/sixiongshan/backdoor/data/deepid/deepid_data_testing.h5")
X_train = utils_keras.preprocess(X_train, preprocess)
X_test = utils_keras.preprocess(X_test, preprocess)
return X_train, Y_train, X_test, Y_test
def load_dataset(data_file):
dataset = utils_keras.load_dataset(data_file)
X_train = dataset['X_train']
Y_train = dataset['Y_train']
X_test = dataset['X_test']
Y_test = dataset['Y_test']
return X_train, Y_train, X_test, Y_test
def load_extractor(name, all_layers=False):
if name is None:
return
m = keras.models.load_model("/home/shansixioing/cloak/models/extractors/{}_extract.h5".format(name))
if all_layers:
if name == 'vggface1':
target_layers = ['conv4_3', 'conv5_1', 'conv5_2', 'conv5_3', 'flatten', 'fc6', 'fc7']
extractor = Model(inputs=m.layers[0].input,
outputs=[m.get_layer(l).output for l in target_layers])
return m
def transfer_learning_model(teacher_model, number_classes):
for l in teacher_model.layers:
l.trainable = False
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 clip_img(X, preprocessing='raw'):
X = utils_keras.reverse_preprocess(X, preprocessing)
X = np.clip(X, 0.0, 255.0)
X = utils_keras.preprocess(X, preprocessing)
return X
def get_dataset_path(dataset):
if dataset == "webface":
train_data_dir = '/mnt/data/sixiongshan/data/webface/train'
test_data_dir = '/mnt/data/sixiongshan/data/webface/test'
number_classes = 10575
number_samples = 475137
elif dataset == "vggface1":
train_data_dir = '/mnt/data/sixiongshan/data/vggface/train'
test_data_dir = '/mnt/data/sixiongshan/data/vggface/test'
number_classes = 2622
number_samples = 1716436 // 3
elif dataset == "vggface2":
train_data_dir = '/mnt/data/sixiongshan/data/vggface2/train'
test_data_dir = '/mnt/data/sixiongshan/data/vggface2/test'
number_classes = 8631
number_samples = 3141890 // 3
elif dataset == "scrub":
train_data_dir = '/mnt/data/sixiongshan/data/facescrub/keras_flow_dir/train'
test_data_dir = '/mnt/data/sixiongshan/data/facescrub/keras_flow_dir/test'
number_classes = 530
number_samples = 57838
elif dataset == "youtubeface":
train_data_dir = '/mnt/data/sixiongshan/data/youtubeface/keras_flow_data/train_mtcnnpy_224'
test_data_dir = '/mnt/data/sixiongshan/data/youtubeface/keras_flow_data/test_mtcnnpy_224'
number_classes = 1283
number_samples = 587137 // 5
elif dataset == "emily":
train_data_dir = '/mnt/data/sixiongshan/data/emface/train'
test_data_dir = '/mnt/data/sixiongshan/data/emface/test'
number_classes = 66
number_samples = 6070
elif dataset == "pubfig":
train_data_dir = '/mnt/data/sixiongshan/data/pubfig/train'
test_data_dir = '/mnt/data/sixiongshan/data/pubfig/test'
number_classes = 65
number_samples = 5979
elif dataset == "iris":
train_data_dir = '/mnt/data/sixiongshan/data/iris/train'
test_data_dir = '/mnt/data/sixiongshan/data/iris/test'
number_classes = 1000
number_samples = 14000
else:
print("Dataset {} does not exist... Abort".format(dataset))
exit(1)
return train_data_dir, test_data_dir, number_classes, number_samples
def large_dataset_loader(dataset, augmentation=False, test_only=False, image_size=(224, 224)):
train_data_dir, test_data_dir, number_classes, number_samples = get_dataset_path(dataset)
train_generator, test_generator = generator_wrap(train_data_dir=train_data_dir, test_data_dir=test_data_dir,
augmentation=augmentation,
test_only=test_only, image_size=image_size)
return train_generator, test_generator, number_classes, number_samples
def sample_from_generator(gen, nb_sample):
x_test, y_test = gen.next()
X_sample = np.zeros((0, x_test.shape[1], x_test.shape[2], x_test.shape[3]))
Y_sample = np.zeros((0, y_test.shape[1]))
while X_sample.shape[0] < nb_sample:
x, y = gen.next()
X_sample = np.concatenate((X_sample, x), axis=0)
Y_sample = np.concatenate((Y_sample, y), axis=0)
X_sample = X_sample[:nb_sample]
Y_sample = Y_sample[:nb_sample]
return X_sample, Y_sample
def generator_wrap(train_data_dir=None, test_data_dir=None, augmentation=False, test_only=False, image_size=(224, 224)):
if not test_data_dir:
validation_split = 0.05
else:
validation_split = 0
if augmentation:
data_gen = ImageDataGenerator(
preprocessing_function=preprocess_input,
rotation_range=20,
width_shift_range=0.15,
height_shift_range=0.15,
shear_range=0.,
zoom_range=0.15,
channel_shift_range=0.,
fill_mode='nearest',
cval=0.,
horizontal_flip=True, validation_split=validation_split)
else:
data_gen = ImageDataGenerator(preprocessing_function=preprocess_input, validation_split=validation_split)
if test_data_dir is None:
train_generator = data_gen.flow_from_directory(
train_data_dir,
target_size=image_size,
batch_size=32, subset='training')
test_generator = data_gen.flow_from_directory(
train_data_dir,
target_size=image_size,
batch_size=32, subset='validation')
else:
if test_only:
train_generator = None
else:
train_generator = data_gen.flow_from_directory(
train_data_dir,
target_size=image_size,
batch_size=32)
test_generator = data_gen.flow_from_directory(
test_data_dir,
target_size=image_size,
batch_size=32)
return train_generator, test_generator
class MergeLayer(Layer):
def __init__(self, **kwargs):
self.result = None
super(MergeLayer, self).__init__(**kwargs)
def build(self, input_shape):
# Create a trainable weight variable for this layer.
kernel_1_shape = (5 * 4 * 60, 160)
kernel_2_shape = (4 * 3 * 80, 160)
bias_shape = (160,)
self.kernel_1 = self.add_weight(name='kernel_1',
shape=kernel_1_shape,
initializer='uniform',
trainable=True)
self.kernel_2 = self.add_weight(name='kernel_2',
shape=kernel_2_shape,
initializer='uniform',
trainable=True)
self.bias = self.add_weight(name='bias',
shape=bias_shape,
initializer='uniform',
trainable=True)
super(MergeLayer, self).build(input_shape) # Be sure to call this at the end
def call(self, x):
layer1 = x[0]
layer2 = x[1]
layer1_r = K.reshape(layer1, (-1, 5 * 4 * 60))
layer2_r = K.reshape(layer2, (-1, 4 * 3 * 80))
self.result = K.dot(layer1_r, self.kernel_1) + \
K.dot(layer2_r, self.kernel_2) + self.bias
return self.result
def compute_output_shape(self, input_shape):
return K.int_shape(self.result)
def load_deepid_model(class_num):
input_shape = (55, 47, 3)
img_input = Input(shape=input_shape)
h1 = Conv2D(20, (4, 4), strides=(1, 1), padding='valid', name='conv_1')(img_input)
h1 = Activation('relu')(h1)
h1 = MaxPooling2D((2, 2), strides=(2, 2), name='pool_1')(h1)
h2 = Conv2D(40, (3, 3), strides=(1, 1), padding='valid', name='conv_2')(h1)
h2 = Activation('relu')(h2)
h2 = MaxPooling2D((2, 2), strides=(2, 2), name='pool_2')(h2)
h3 = Conv2D(60, (3, 3), strides=(1, 1), padding='valid', name='conv_3')(h2)
h3 = Activation('relu')(h3)
h3 = MaxPooling2D((2, 2), strides=(2, 2), name='pool_3')(h3)
h4 = Conv2D(80, (2, 2), strides=(1, 1), padding='valid', name='conv_4')(h3)
h4 = Activation('relu')(h4)
h5 = MergeLayer()([h3, h4])
h5 = Activation('relu')(h5)
h5 = Dense(class_num, name='fc')(h5)
h5 = Activation('softmax')(h5)
inputs = img_input
model = Model(inputs, h5, name='vgg_face')
return model
def get_label_data(X, Y, target):
X_filter = np.array(X)
Y_filter = np.array(Y)
remain_idx = np.argmax(Y, axis=1) == target
X_filter = X_filter[remain_idx]
Y_filter = Y_filter[remain_idx]
return X_filter, Y_filter
def get_other_label_data(X, Y, target):
X_filter = np.array(X)
Y_filter = np.array(Y)
remain_idx = np.argmax(Y, axis=1) != target
X_filter = X_filter[remain_idx]
Y_filter = Y_filter[remain_idx]
return X_filter, Y_filter
def get_labels_data(X, Y, target_ls):
assert isinstance(target_ls, list)
X_filter = np.array(X)
Y_filter = np.array(Y)
remain_idx = np.array([False] * len(Y_filter))
for target in target_ls:
cur_remain_idx = np.argmax(Y, axis=1) == target
remain_idx = np.logical_or(remain_idx, cur_remain_idx)
X_filter = X_filter[remain_idx]
Y_filter = Y_filter[remain_idx]
return X_filter, Y_filter
def get_other_labels_data_except(X, Y, target_ls):
assert isinstance(target_ls, list)
X_filter = np.array(X)
Y_filter = np.array(Y)
remain_idx = np.array([True] * len(Y_filter))
for target in target_ls:
cur_remain_idx = np.argmax(Y, axis=1) != target
remain_idx = np.logical_and(remain_idx, cur_remain_idx)
X_filter = X_filter[remain_idx]
Y_filter = Y_filter[remain_idx]
return X_filter, Y_filter
def get_bottom_top_model(model, layer_name):
layer = model.get_layer(layer_name)
bottom_input = Input(model.input_shape[1:])
bottom_output = bottom_input
top_input = Input(layer.output_shape[1:])
top_output = top_input
bottom = True
for layer in model.layers:
if bottom:
bottom_output = layer(bottom_output)
else:
top_output = layer(top_output)
if layer.name == layer_name:
bottom = False
bottom_model = Model(bottom_input, bottom_output)
top_model = Model(top_input, top_output)
return bottom_model, top_model
def load_end2end_model(arch, number_classes):
if arch == 'resnet':
MODEL = keras.applications.resnet_v2.ResNet152V2(include_top=False, weights='imagenet', pooling='avg',
input_shape=(224, 224, 3))
elif arch == 'inception':
MODEL = keras.applications.InceptionResNetV2(include_top=False, weights='imagenet', pooling='avg',
input_shape=(224, 224, 3))
elif arch == 'mobile':
MODEL = keras.applications.mobilenet_v2.MobileNetV2(include_top=False, weights='imagenet', pooling='avg',
input_shape=(224, 224, 3))
elif arch == 'dense':
MODEL = keras.applications.densenet.DenseNet121(include_top=False, weights='imagenet', pooling='avg',
input_shape=(224, 224, 3))
model = load_victim_model(number_classes, MODEL, end2end=True)
return model
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 add_last_layer(number_classes, teacher_model, cut_to_layer=None):
for l in teacher_model.layers:
l.trainable = False
if cut_to_layer:
x = teacher_model.layers[cut_to_layer].output
print(teacher_model.layers[cut_to_layer].name)
else:
x = teacher_model.layers[-1].output
x = Dense(number_classes, name='softmax')(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 resize_batch(x, target_size=(224, 224), intensity="imagenet"):
if x.shape[:2] == target_size:
return x
x = utils_keras.reverse_preprocess(x, intensity)
resized = np.array([resize(a, target_size) for a in x])
return utils_keras.preprocess(resized, intensity)
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 split_dataset(X, y, ratio=0.3):
x_appro, x_later, y_appro, y_later = train_test_split(X, y, test_size=ratio, random_state=0)
return x_appro, x_later, y_appro, y_later
def data_generator(X, Y, batch_size=32, target_size=(224, 224), intensity='imagenet'):
data_gen = ImageDataGenerator()
data_gen = data_gen.flow(X, Y, batch_size=batch_size)
while True:
cur_X, cur_Y = next(data_gen)
cur_X = resize_batch(cur_X, target_size=target_size, intensity=intensity)
yield np.array(cur_X), cur_Y
def evaluate(model, X_test, Y_test, batch_size=32, target_size=(224, 224)):
test_other_gen = data_generator(X_test, Y_test, batch_size=batch_size, target_size=target_size)
if len(X_test) < batch_size * 2:
batch_size = 1
test_other_step = len(X_test) // batch_size // 2
acc = model.evaluate_generator(test_other_gen, steps=test_other_step, verbose=0)[1]
return acc
def normalize(x):
return x / np.linalg.norm(x, axis=1, keepdims=True)
class CloakData(object):
def __init__(self, dataset, img_shape=(224, 224), target_selection_tries=30, protect_class=None):
self.dataset = dataset
self.img_shape = img_shape
self.target_selection_tries = target_selection_tries
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])
print("Protect label: {} | Sybil label: {}".format(self.protect_class, self.sybil_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.target_path, self.target_data = self.select_target_label()
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 | {} sybil images".format(len(self.protect_class_path), len(self.sybil_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("/home/shansixioing/cloak/embs/{}_emb_norm.p".format(extractor_name), "rb"))
# path2emb = pickle.load(open("/home/shansixioing/cloak/embs/vggface2_inception_emb.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 = extractor.predict(self.protect_train_X)
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())
teacher_dataset = feature_extractors_names[0].split("_")[0]
# items = [(k, v) for k, v in path2emb.items() if teacher_dataset in k]
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, 'l2')
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[:2]:
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

119
fawkes/eval_cloak.py
View File

@ -4,30 +4,23 @@ sys.path.append("/home/shansixioing/tools/")
sys.path.append("/home/shansixioing/cloak/")
import argparse
import gen_utils
from tensorflow import set_random_seed
from encode_utils import *
from utils import init_gpu, load_extractor, load_victim_model, dump_dictionary_as_json
import os
import numpy as np
import random
import pickle
import re
import locale
from keras.preprocessing import image
from keras.utils import to_categorical
from keras.applications.vgg16 import preprocess_input
loc = locale.getlocale()
locale.setlocale(locale.LC_ALL, loc)
# import locale
#
# loc = locale.getlocale()
# locale.setlocale(locale.LC_ALL, loc)
SEEDS = [12345, 23451, 34512, 45123, 51234, 54321, 43215, 32154, 21543, 15432]
IMG_SHAPE = [224, 224, 3]
MODEL = {
'vggface1_inception': "0",
'vggface1_dense': "1",
"vggface2_inception": "2",
"vggface2_dense": "3",
"webface_dense": "4",
"webface_inception": "5",
}
RES_DIR = '/home/shansixioing/cloak/results/'
def select_samples(data_dir):
@ -40,40 +33,30 @@ def select_samples(data_dir):
return all_data_path
def generator_wrap(cloak_data, n_uncloaked, n_classes, test=False, validation_split=0.1):
def generator_wrap(cloak_data, n_classes, test=False, validation_split=0.1):
if test:
# all_data_path = cloak_data.all_test_path
all_data_path = select_samples(cloak_data.test_data_dir)
else:
# all_data_path = cloak_data.all_training_path
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]
if cloak_data.cloaked_sybil_train_X is not None:
cloaked_sybil_X = cloak_data.cloaked_sybil_train_X #[:args.number_sybil * 131]
#
# for _ in range(len(cloaked_sybil_X) - 131):
# all_data_path.append(cloak_data.sybil_class_path[0])
# random seed for selecting uncloaked pictures
np.random.seed(12345)
uncloaked_path = np.random.choice(cloak_data.protect_class_path, n_uncloaked).tolist()
# all_vals = list(cloak_data.path2idx.items())
while True:
batch_X = []
batch_Y = []
cur_batch_path = np.random.choice(all_data_path, args.batch_size)
for p in cur_batch_path:
# p = p.encode("utf-8").decode("ascii", 'ignore')
cur_y = cloak_data.path2idx[p]
# protect class and sybil class do not need to appear in test dataset
if test and (re.search(cloak_data.protect_class, p) or re.search(cloak_data.sybil_class, p)):
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 and p not in uncloaked_path:
elif p in cloak_data.protect_class_path:
cur_x = random.choice(cloaked_train_X)
# sybil class in train dataset
elif p in cloak_data.sybil_class_path and cloak_data.cloaked_sybil_train_X is not None:
cur_x = random.choice(cloaked_sybil_X)
else:
im = image.load_img(p, target_size=cloak_data.img_shape)
im = image.img_to_array(im)
@ -108,45 +91,46 @@ def main():
SEED = SEEDS[args.seed_idx]
random.seed(SEED)
set_random_seed(SEED)
gen_utils.init_gpu(args.gpu)
init_gpu(args.gpu)
if args.dataset == 'pubfig':
N_CLASSES = 65
CLOAK_DIR = "{}_tm{}_tgt57_r1.0_th{}".format(args.dataset, args.model_idx, args.th)
CLOAK_DIR = args.cloak_data
elif args.dataset == 'scrub':
N_CLASSES = 530
CLOAK_DIR = "{}_tm{}_tgtPatrick_Dempsey_r1.0_th{}_joint".format(args.dataset, args.model_idx, args.th)
elif args.dataset == 'webface':
N_CLASSES = 10575
CLOAK_DIR = "{}_tm{}_tgt1640351_r1.0_th0.01/".format(args.dataset, args.model_idx)
CLOAK_DIR = args.cloak_data
else:
raise ValueError
CLOAK_DIR = CLOAK_DIR + "_th{}_sd{}".format(args.th, int(args.sd))
print(CLOAK_DIR)
CLOAK_DIR = os.path.join(RES_DIR, CLOAK_DIR)
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 = {}
train_generator = generator_wrap(cloak_data, n_uncloaked=args.n_uncloaked, n_classes=N_CLASSES,
train_generator = generator_wrap(cloak_data, n_classes=N_CLASSES,
validation_split=args.validation_split)
test_generator = generator_wrap(cloak_data, test=True, n_uncloaked=args.n_uncloaked, n_classes=N_CLASSES,
test_generator = generator_wrap(cloak_data, test=True, n_classes=N_CLASSES,
validation_split=args.validation_split)
EVAL_RES['transfer_model'] = args.transfer_model
if args.end2end:
model = load_end2end_model("dense", N_CLASSES)
else:
base_model = load_extractor(args.transfer_model)
model = load_victim_model(teacher_model=base_model, number_classes=N_CLASSES)
base_model = load_extractor(args.transfer_model)
model = load_victim_model(teacher_model=base_model, number_classes=N_CLASSES)
original_X, original_Y = eval_uncloaked_test_data(cloak_data, N_CLASSES)
cloaked_test_X, cloaked_test_Y = eval_cloaked_test_data(cloak_data, N_CLASSES,
validation_split=args.validation_split)
model.fit_generator(train_generator, steps_per_epoch=cloak_data.number_samples // 32,
validation_data=(original_X, original_Y), epochs=args.n_epochs, verbose=2,
use_multiprocessing=True, workers=3)
try:
model.fit_generator(train_generator, steps_per_epoch=cloak_data.number_samples // 32,
validation_data=(original_X, original_Y), epochs=args.n_epochs, verbose=1,
use_multiprocessing=False, workers=1)
except KeyboardInterrupt:
pass
_, acc_original = model.evaluate(original_X, original_Y, verbose=0)
print("Accuracy on uncloaked/original images TEST: {:.4f}".format(acc_original))
@ -156,47 +140,38 @@ def main():
print("Accuracy on cloaked images TEST: {:.4f}".format(acc_cloaked))
EVAL_RES['acc_cloaked'] = acc_cloaked
# pred = model.predict_generator(test_generator, verbose=0, steps=10)
# pred = np.argmax(pred, axis=1)
# print(pred)
_, 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
gen_utils.dump_dictionary_as_json(EVAL_RES,
os.path.join(CLOAK_DIR, "{}_eval_sybil_uncloaked{}_seed{}_th{}.json".format(
args.transfer_model, args.end2end, args.seed_idx, args.th)))
dump_dictionary_as_json(EVAL_RES,
os.path.join(CLOAK_DIR, "eval_seed{}_th{}.json".format(args.seed_idx, args.th)))
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str,
help='GPU id', default='1')
parser.add_argument('--n_uncloaked', type=int,
help='number of uncloaked images', default=0)
help='GPU id', default='2')
parser.add_argument('--seed_idx', type=int,
help='random seed index', default=0)
parser.add_argument('--dataset', type=str,
help='name of dataset', default='pubfig')
parser.add_argument('--model_idx', type=str,
help='teacher model index', default="2")
help='name of dataset', default='scrub')
parser.add_argument('--cloak_data', type=str,
help='name of the cloak result directory',
default='scrub_webface_dense_robust_protectPatrick_Dempsey')
parser.add_argument('--sd', type=int, default=1e6)
parser.add_argument('--th', type=float, default=0.01)
parser.add_argument('--transfer_model', type=str,
help='student model', default='vggface2_inception')
parser.add_argument('--end2end', type=int,
help='whether use end2end', default=0)
help='student model', default='../feature_extractors/vggface2_inception_extract.h5')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--validation_split', type=float, default=0.1)
parser.add_argument('--use_sybil', type=int,
help='whether use sybil class', default=0)
parser.add_argument('--number_sybil', type=int,
help='whether use sybil class', default=1)
parser.add_argument('--n_epochs', type=int, default=3)
parser.add_argument('--th', type=float, default=0.01)
parser.add_argument('--limit', type=int, default=0)
return parser.parse_args(argv)
if __name__ == '__main__':
args = parse_arguments(sys.argv[1:])
main()
# python3 eval_cloak.py --gpu 2 --n_uncloaked 0 --dataset pubfig --model_idx 5 --transfer_model webface_inception
# python3 eval_cloak.py --gpu 2 --n_uncloaked 0 --dataset pubfig --model_idx 5 --transfer_model webface_inception

110
fawkes/protection.py
View File

@ -1,56 +1,35 @@
import argparse
import os
import pickle
import random
import sys
sys.path.append("/home/shansixioing/tools/")
sys.path.append("/home/shansixioing/cloak/")
import argparse
from tensorflow import set_random_seed
from .differentiator import FawkesMaskGeneration
import os
import numpy as np
import random
import pickle
from .utils import load_extractor, CloakData, init_gpu
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)
SYBIL_ONLY = False
NUM_IMG_PROTECTED = 20 # Number of images used to optimize the target class
BATCH_SIZE = 20
MODEL_IDX = {
'vggface1_inception': "0",
'vggface1_dense': "1",
"vggface2_inception": "2",
"vggface2_dense": "3",
"webface_dense": "4",
"webface_inception": "5",
}
IDX2MODEL = {v: k for k, v in MODEL_IDX.items()}
NUM_IMG_PROTECTED = 10 # Number of images used to optimize the target class
BATCH_SIZE = 10
IMG_SHAPE = [224, 224, 3]
GLOBAL_MASK = 0
MAXIMIZE = False
MAX_ITER = 500
INITIAL_CONST = 1e6
LR = 0.1
MAX_ITER = 1000
def diff_protected_data(sess, feature_extractors_ls, image_X, number_protect, target_X=None, sybil=False, th=0.01):
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=INITIAL_CONST,
learning_rate=LR,
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:])
@ -62,61 +41,43 @@ def diff_protected_data(sess, feature_extractors_ls, image_X, number_protect, ta
return cloaked_image_X
def save_results(RES, path):
pickle.dump(RES, open(path, "wb"))
def perform_defense():
RES = {}
sess = init_gpu(args.gpu)
DSSIM_THRESHOLD = args.th
FEATURE_EXTRACTORS = [IDX2MODEL[args.model_idx]]
MODEL_HASH = "".join(MODEL_IDX[m] for m in FEATURE_EXTRACTORS)
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(FEATURE_EXTRACTORS)
feature_extractors_ls = [load_extractor(name) for name in FEATURE_EXTRACTORS]
print("Loading {} for optimization".format(args.feature_extractor))
feature_extractors_ls = [load_extractor(name, layer_idx=args.layer_idx) for name in FEATURE_EXTRACTORS]
protect_class = args.protect_class
cloak_data = CloakData(args.dataset, target_selection_tries=1, protect_class=protect_class)
model_name = args.feature_extractor.split("/")[-1].split('.')[0].replace("_extract", "")
RES_FILE_NAME = "{}_{}_protect{}_th{}_sd{}".format(args.dataset, model_name, cloak_data.protect_class, args.th,
args.sd)
RES_FILE_NAME = os.path.join(RES_DIR, RES_FILE_NAME)
if os.path.exists(RES_FILE_NAME):
exit(1)
print("Protect Class: ", cloak_data.protect_class)
if "robust" in FEATURE_EXTRACTORS[0]:
non_robust = MODEL_IDX["_".join(FEATURE_EXTRACTORS[0].split("_")[:2])]
if args.dataset == 'pubfig':
CLOAK_DIR = 'pubfig_tm{}_tgt57_r1.0_th0.01'.format(non_robust)
CLOAK_DIR = os.path.join(RES_DIR, CLOAK_DIR)
RES = pickle.load(open(os.path.join(CLOAK_DIR, "cloak_data.p"), 'rb'))
cloak_data = RES['cloak_data']
elif args.dataset == 'scrub':
CLOAK_DIR = 'scrub_tm{}_tgtPatrick_Dempsey_r1.0_th0.01'.format(non_robust)
CLOAK_DIR = os.path.join(RES_DIR, CLOAK_DIR)
RES = pickle.load(open(os.path.join(CLOAK_DIR, "cloak_data.p"), 'rb'))
cloak_data = RES['cloak_data']
else:
cloak_data.target_path, cloak_data.target_data = cloak_data.select_target_label(feature_extractors_ls,
FEATURE_EXTRACTORS)
cloak_data.target_path, cloak_data.target_data = cloak_data.select_target_label(feature_extractors_ls,
FEATURE_EXTRACTORS)
RES_FILE_NAME = "{}_tm{}_tgt{}_r{}_th{}".format(args.dataset, MODEL_HASH, cloak_data.protect_class, RATIO,
DSSIM_THRESHOLD)
RES_FILE_NAME = os.path.join(RES_DIR, RES_FILE_NAME)
os.makedirs(RES_DIR, exist_ok=True)
os.makedirs(RES_FILE_NAME, exist_ok=True)
print("Protect Current Label Data...")
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, sybil=False, th=DSSIM_THRESHOLD)
target_X=cloak_data.target_data, th=args.th)
cloak_data.cloaked_protect_train_X = cloak_image_X
RES['cloak_data'] = cloak_data
save_results(RES, os.path.join(RES_FILE_NAME, 'cloak_data.p'))
pickle.dump(RES, open(os.path.join(RES_FILE_NAME, 'cloak_data.p'), "wb"))
def parse_arguments(argv):
@ -124,11 +85,18 @@ 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='pubfig')
parser.add_argument('--model_idx', type=str,
help='teacher model index', default="3")
help='name of dataset', default='scrub')
parser.add_argument('--feature-extractor', type=str,
help="name of the feature extractor used for optimization",
default="../feature_extractors/webface_dense_robust_extract.h5")
parser.add_argument('--layer-idx', type=int,
help="the idx of the layer of neuron that are used as feature space",
default=-3)
parser.add_argument('--th', type=float, default=0.01)
parser.add_argument('--sd', type=int, default=1e4)
parser.add_argument('--protect_class', type=str, default=None)
parser.add_argument('--lr', type=float, default=0.1)
return parser.parse_args(argv)

134
fawkes/utils.py
View File

@ -1,3 +1,4 @@
import json
import os
import pickle
import random
@ -7,11 +8,26 @@ 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
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)
@ -25,6 +41,19 @@ def fix_gpu_memory(mem_fraction=1):
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])
@ -152,93 +181,43 @@ def imagenet_reverse_preprocessing(x, data_format=None):
return x
def imagenet_reverse_preprocessing_cntk(x, data_format=None):
import keras.backend as K
""" Reverse preprocesses a tensor encoding a batch of images.
# Arguments
x: input Numpy tensor, 4D.
data_format: data format of the image tensor.
# Returns
Preprocessed tensor.
"""
x = np.array(x)
if data_format is None:
data_format = K.image_data_format()
assert data_format in ('channels_last', 'channels_first')
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
if data_format == 'channels_first':
# Zero-center by mean pixel
x[:, 0, :, :] += 114.0
x[:, 1, :, :] += 114.0
x[:, 2, :, :] += 114.0
# 'BGR'->'RGB'
x = x[:, ::-1, :, :]
def load_extractor(name, layer_idx=None):
model = keras.models.load_model(name)
if "extract" in name.split("/")[-1]:
model = keras.models.load_model(name)
else:
# Zero-center by mean pixel
x[:, :, :, 0] += 114.0
x[:, :, :, 1] += 114.0
x[:, :, :, 2] += 114.0
# 'BGR'->'RGB'
x = x[:, :, :, ::-1]
return x
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")
def load_extractor(name):
model = keras.models.load_model("/home/shansixioing/cloak/models/extractors/{}_extract.h5".format(name))
return model
def get_dataset_path(dataset):
if dataset == "webface":
train_data_dir = '/mnt/data/sixiongshan/data/webface/train'
test_data_dir = '/mnt/data/sixiongshan/data/webface/test'
number_classes = 10575
number_samples = 475137
elif dataset == "vggface1":
train_data_dir = '/mnt/data/sixiongshan/data/vggface/train'
test_data_dir = '/mnt/data/sixiongshan/data/vggface/test'
number_classes = 2622
number_samples = 1716436 // 3
elif dataset == "vggface2":
train_data_dir = '/mnt/data/sixiongshan/data/vggface2/train'
test_data_dir = '/mnt/data/sixiongshan/data/vggface2/test'
number_classes = 8631
number_samples = 3141890 // 3
elif dataset == "scrub":
train_data_dir = '/mnt/data/sixiongshan/data/facescrub/keras_flow_dir/train'
test_data_dir = '/mnt/data/sixiongshan/data/facescrub/keras_flow_dir/test'
if dataset == "scrub":
train_data_dir = '../data/scrub/train'
test_data_dir = '../data/scrub/test'
number_classes = 530
number_samples = 57838
elif dataset == "youtubeface":
train_data_dir = '/mnt/data/sixiongshan/data/youtubeface/keras_flow_data/train_mtcnnpy_224'
test_data_dir = '/mnt/data/sixiongshan/data/youtubeface/keras_flow_data/test_mtcnnpy_224'
number_classes = 1283
number_samples = 587137 // 5
elif dataset == "emily":
train_data_dir = '/mnt/data/sixiongshan/data/emface/train'
test_data_dir = '/mnt/data/sixiongshan/data/emface/test'
number_classes = 66
number_samples = 6070
elif dataset == "pubfig":
train_data_dir = '/mnt/data/sixiongshan/data/pubfig/train'
test_data_dir = '/mnt/data/sixiongshan/data/pubfig/test'
train_data_dir = '../data/pubfig/train'
test_data_dir = '../data/pubfig/test'
number_classes = 65
number_samples = 5979
elif dataset == "iris":
train_data_dir = '/mnt/data/sixiongshan/data/iris/train'
test_data_dir = '/mnt/data/sixiongshan/data/iris/test'
number_classes = 1000
number_samples = 14000
else:
print("Dataset {} does not exist... Abort".format(dataset))
exit(1)
raise Exception(
"Dataset {} does not exist, please download to data/ and add the path to this function... Abort".format(
dataset))
return train_data_dir, test_data_dir, number_classes, number_samples
@ -261,7 +240,6 @@ class CloakData(object):
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])
print("Protect label: {} | Sybil label: {}".format(self.protect_class, self.sybil_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)
@ -290,11 +268,11 @@ class CloakData(object):
def load_embeddings(self, feature_extractors_names):
dictionaries = []
for extractor_name in feature_extractors_names:
path2emb = pickle.load(open("/home/shansixioing/cloak/embs/{}_emb_norm.p".format(extractor_name), "rb"))
# path2emb = pickle.load(open("/home/shansixioing/cloak/embs/vggface2_inception_emb.p".format(extractor_name), "rb"))
extractor_name = extractor_name.split("/")[-1].split('.')[0].replace("_extract", "")
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]