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):
if mode == 'custom':
pass
else:
th, max_step, lr = self.mode2param(mode)
start_time = time.time()
if not image_paths:
raise Exception("No images in the directory")
with graph.as_default():
faces = Faces(image_paths, self.aligner, verbose=1)
original_images = faces.cropped_faces
original_images = np.array(original_images)
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(self.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)
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, cloaked_img, path in zip(final_images, protected_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('attack cost %f s' % elapsed_time)
print("Done!")
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 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,
unprotected,
protected,
image,
mode='min',
th=0.04,
sd=1e9,
lr=10,
max_step=500,
batch_size=1,
format='png',
separate_target=True,
debug=False,
no_align=False,
lang="spanish"):
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
]
])
img = load_image(unprotected + "/" + image)
with graph.as_default():
faces = Faces([unprotected + "/" + image], [img],
self.aligner,
verbose=1,
no_align=no_align)
original_images = faces.cropped_faces
if len(original_images) == 0:
response = {
'module': 'makeup',
'status': 'No face detected',
'd1': 'No face detected',
'd2': 'Unhandled error',
'd3': 'No face detected'
}
return response
original_images = np.array(original_images)
with sess.as_default():
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
final_images = faces.merge_faces(
reverse_process_cloaked(protected_images),
reverse_process_cloaked(original_images))
backend_img = protected + "/" + image
dump_image(final_images[0], backend_img, format=format)
if (lang == "english"):
response = {
'module':
'makeup',
'status':
'MakeUp Ok',
'mode':
mode,
'd1':
'Face attacked',
'd2':
'Mode: {}'.format(mode),
'd3':
'The face of this avatar has been attacked ' +
'and although it looks identical to the original ' +
'photo the reality is that you will see that ' +
'the face has alterations that make it different ' +
'from the original photo.',
'backend_img':
backend_img
}
else:
response = {
'module':
'makeup',
'status':
'MakeUp Ok',
'mode':
mode,
'd1':
'Rostro atacado',
'd2':
'Modo: {}'.format(mode),
'd3':
'El rostro de este avatar ha sido atacado y ' +
'aunque parezca idéntico a la foto original la ' +
'realidad es que verá que el rostro tiene altera' +
'ciones que la hacen diferente a la foto original.',
'backend_img':
backend_img
}
response["url_img"] = backend_img[backend_img.find("/static"):]
return response