def run_protection(self, image_paths, th=0.04, sd=1e7, lr=10, max_step=500, batch_size=1, format='png',
separate_target=True, debug=False, no_align=False, exp="", maximize=True,
save_last_on_failed=True):
current_param = "-".join([str(x) for x in [self.th, sd, self.lr, self.max_step, batch_size, format,
separate_target, debug]])
image_paths, loaded_images = filter_image_paths(image_paths)
if not image_paths:
print("No images in the directory")
return 3
faces = Faces(image_paths, loaded_images, self.aligner, verbose=1, no_align=no_align)
original_images = faces.cropped_faces
if len(original_images) == 0:
print("No face detected. ")
return 2
original_images = np.array(original_images)
if current_param != self.protector_param:
self.protector_param = current_param
if self.protector is not None:
del self.protector
if batch_size == -1:
batch_size = len(original_images)
self.protector = FawkesMaskGeneration(self.feature_extractors_ls,
batch_size=batch_size,
mimic_img=True,
intensity_range=PREPROCESS,
initial_const=sd,
learning_rate=self.lr,
max_iterations=self.max_step,
l_threshold=self.th,
verbose=debug,
maximize=maximize,
keep_final=False,
image_shape=(IMG_SIZE, IMG_SIZE, 3),
loss_method='features',
tanh_process=True,
save_last_on_failed=save_last_on_failed,
)
protected_images = generate_cloak_images(self.protector, original_images)
faces.cloaked_cropped_faces = protected_images
final_images, images_without_face = faces.merge_faces(
reverse_process_cloaked(protected_images, preprocess=PREPROCESS),
reverse_process_cloaked(original_images, preprocess=PREPROCESS))
for i in range(len(final_images)):
if i in images_without_face:
continue
p_img = final_images[i]
path = image_paths[i]
file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), self.mode, format)
dump_image(p_img, file_name, format=format)
print("Done!")
return 1
def run_protection(self,
image_paths,
mode='min',
th=0.04,
sd=1e9,
lr=10,
max_step=500,
batch_size=1,
format='png',
separate_target=True,
debug=True,
no_align=False):
if mode == 'custom':
pass
else:
th, max_step, lr = self.mode2param(mode)
current_param = "-".join([
str(x) for x in [
mode, th, sd, lr, max_step, batch_size, format,
separate_target, debug
]
])
print("Fininshed Setting Parameters at: {}".format(datetime.now()))
image_paths, loaded_images = filter_image_paths(image_paths)
print("Finished Loading Images at: {}".format(datetime.now()))
if not image_paths:
print("No images in the directory")
return 3
with graph.as_default():
faces = Faces(image_paths,
loaded_images,
self.aligner,
verbose=1,
no_align=no_align)
original_images = faces.cropped_faces
print("Finished Detecting Faces In The Images at: {}".format(
datetime.now()))
if len(original_images) == 0:
print("No face detected. ")
return 2
original_images = np.array(original_images)
with sess.as_default():
if separate_target:
target_embedding = []
i = 0
print("Start image reshape at: {}".format(datetime.now()))
for org_img in original_images:
org_img = org_img.reshape([1] + list(org_img.shape))
print("Finished Image {} Reshape at: {}".format(
i, datetime.now()))
tar_emb = select_target_label(
org_img, self.feature_extractors_ls, self.fs_names)
print(
"Finished Target Embedding Image {} at: {}".format(
i, datetime.now()))
target_embedding.append(tar_emb)
i += 1
target_embedding = np.concatenate(target_embedding)
else:
target_embedding = select_target_label(
original_images, self.feature_extractors_ls,
self.fs_names)
print("Finished All Images Target Embedding at: {}".format(
datetime.now()))
if current_param != self.protector_param:
self.protector_param = current_param
if self.protector is not None:
del self.protector
self.protector = FawkesMaskGeneration(
sess,
self.feature_extractors_ls,
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 if debug else 0,
maximize=False,
keep_final=False,
image_shape=(224, 224, 3))
print("Finished Initializing Fawkes Protector at: {}".format(
datetime.now()))
protected_images = generate_cloak_images(
self.protector,
original_images,
target_emb=target_embedding)
print("Finished Protecting Images at: {}".format(
datetime.now()))
faces.cloaked_cropped_faces = protected_images
final_images = faces.merge_faces(
reverse_process_cloaked(protected_images),
reverse_process_cloaked(original_images))
print("Finished Merging Faces at: {}".format(datetime.now()))
for p_img, path in zip(final_images, image_paths):
file_name = "{}_{}_cloaked.{}".format(
".".join(path.split(".")[:-1]), mode, format)
dump_image(p_img, file_name, format=format)
print("Done! and Finished Exporting File at: {}".format(
datetime.now()))
return 1
def main():
sess = init_gpu(args.gpu)
ali = aligner(sess)
print("Build attacker's model")
image_paths = glob.glob(os.path.join(args.directory, "*"))
cloak_file_name = "_cloaked"
original_image_paths = sorted(
[path for path in image_paths if "cloaked" not in path.split("/")[-1]])
original_image_paths, original_loaded_images = filter_image_paths(
original_image_paths)
protect_image_paths = sorted([
path for path in image_paths if cloak_file_name in path.split("/")[-1]
])
protect_image_paths, protected_loaded_images = filter_image_paths(
protect_image_paths)
print("Find {} original image and {} cloaked images".format(
len(original_image_paths), len(protect_image_paths)))
original_faces = Faces(original_image_paths,
original_loaded_images,
ali,
verbose=1,
eval_local=True)
original_faces = original_faces.cropped_faces
cloaked_faces = Faces(protect_image_paths,
protected_loaded_images,
ali,
verbose=1,
eval_local=True)
cloaked_faces = cloaked_faces.cropped_faces
if len(original_faces) <= 10 or len(protect_image_paths) <= 10:
raise Exception(
"Must have more than 10 protected images to run the evaluation")
num_classes = args.num_other_classes + 1
datagen = DataGenerator(original_faces, cloaked_faces)
original_test_X, original_test_Y = datagen.test_original()
print("{} Training Images | {} Testing Images".format(
len(datagen.protect_images_train), len(original_test_X)))
train_generator = datagen.generate()
test_generator = datagen.generate(test=True)
base_model = load_extractor(args.base_model)
model = load_victim_model(teacher_model=base_model,
number_classes=num_classes)
cb = CallbackGenerator(original_imgs=original_test_X,
protect_imgs=cloaked_faces,
original_y=original_test_Y,
original_protect_y=None,
test_gen=test_generator)
model.fit_generator(train_generator,
steps_per_epoch=num_classes * 10 // 32,
epochs=args.n_epochs,
verbose=1,
callbacks=[cb])
_, acc_original = model.evaluate(original_test_X,
original_test_Y,
verbose=0)
print("Protection Success Rate: {:.4f}".format(1 - acc_original))
def run_protection(self,
image_paths,
mode='low',
th=0.04,
sd=1e9,
lr=10,
max_step=500,
batch_size=1,
format='png',
separate_target=True,
debug=False):
if mode == 'custom':
pass
else:
th, max_step, lr = self.mode2param(mode)
current_param = "-".join([
str(x) for x in [
mode, th, sd, lr, max_step, batch_size, format,
separate_target, debug
]
])
image_paths, loaded_images = filter_image_paths(image_paths)
if not image_paths:
raise Exception("No images in the directory")
with graph.as_default():
faces = Faces(image_paths, loaded_images, self.aligner, verbose=1)
original_images = faces.cropped_faces
original_images = np.array(original_images)
with sess.as_default():
if separate_target:
target_embedding = []
for org_img in original_images:
org_img = org_img.reshape([1] + list(org_img.shape))
tar_emb = select_target_label(
org_img, self.feature_extractors_ls, self.fs_names)
target_embedding.append(tar_emb)
target_embedding = np.concatenate(target_embedding)
else:
target_embedding = select_target_label(
original_images, self.feature_extractors_ls,
self.fs_names)
if current_param != self.protector_param:
self.protector_param = current_param
if self.protector is not None:
del self.protector
self.protector = FawkesMaskGeneration(
sess,
self.feature_extractors_ls,
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 if debug else 0,
maximize=False,
keep_final=False,
image_shape=(224, 224, 3))
protected_images = generate_cloak_images(
self.protector,
original_images,
target_emb=target_embedding)
faces.cloaked_cropped_faces = protected_images
cloak_perturbation = reverse_process_cloaked(
protected_images) - reverse_process_cloaked(
original_images)
final_images = faces.merge_faces(cloak_perturbation)
for p_img, path in zip(final_images, image_paths):
file_name = "{}_{}_cloaked.{}".format(
".".join(path.split(".")[:-1]), mode, format)
dump_image(p_img, file_name, format=format)
print("Done!")
return None
def run_protection(self,
image_paths,
mode='mid',
th=0.04,
sd=1e9,
lr=10,
max_step=500,
batch_size=1,
format='png',
separate_target=True,
debug=False):
if mode == 'custom':
pass
else:
th, max_step, lr = self.mode2param(mode)
image_paths, loaded_images = filter_image_paths(image_paths)
if not image_paths:
raise Exception("No images in the directory")
with graph.as_default():
faces = Faces(image_paths, loaded_images, self.aligner, verbose=1)
original_images = faces.cropped_faces
original_images = np.array(original_images)
with sess.as_default():
if separate_target:
target_embedding = []
for org_img in original_images:
org_img = org_img.reshape([1] + list(org_img.shape))
tar_emb = select_target_label(
org_img, self.feature_extractors_ls, self.fs_names)
target_embedding.append(tar_emb)
target_embedding = np.concatenate(target_embedding)
else:
target_embedding = select_target_label(
original_images, self.feature_extractors_ls,
self.fs_names)
protected_images = generate_cloak_images(
sess,
self.feature_extractors_ls,
original_images,
target_emb=target_embedding,
th=th,
faces=faces,
sd=sd,
lr=lr,
max_step=max_step,
batch_size=batch_size,
debug=debug)
faces.cloaked_cropped_faces = protected_images
cloak_perturbation = reverse_process_cloaked(
protected_images) - reverse_process_cloaked(
original_images)
final_images = faces.merge_faces(cloak_perturbation)
for p_img, path in zip(final_images, image_paths):
file_name = "{}_{}_cloaked.{}".format(
".".join(path.split(".")[:-1]), mode, format)
dump_image(p_img, file_name, format=format)
# elapsed_time = time.time() - start_time
print("Done!")
return None
