ResearchPapers/fawkes
ResearchPapers/fawkes
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add debug option and progbar

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Shawn-Shan 2020-07-19 20:54:04 -05:00
parent 5b013a01fc
commit 6fe3a7c3fd
4 changed files with 140 additions and 128 deletions
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7
fawkes/__init__.py
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@ -4,17 +4,18 @@
# @Link : https://www.shawnshan.com/ # @Link : https://www.shawnshan.com/
__version__ = '0.0.8' __version__ = '0.0.9'
from .detect_faces import create_mtcnn, run_detect_face from .detect_faces import create_mtcnn, run_detect_face
from .differentiator import FawkesMaskGeneration from .differentiator import FawkesMaskGeneration
from .protection import main, Fawkes from .protection import main, Fawkes
from .utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, Faces, get_file from .utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, Faces, get_file, \
filter_image_paths
__all__ = ( __all__ = (
'__version__', 'create_mtcnn', 'run_detect_face', '__version__', 'create_mtcnn', 'run_detect_face',
'FawkesMaskGeneration', 'load_extractor', 'FawkesMaskGeneration', 'load_extractor',
'init_gpu', 'init_gpu',
'select_target_label', 'dump_image', 'reverse_process_cloaked', 'select_target_label', 'dump_image', 'reverse_process_cloaked',
'Faces', 'get_file', 'main', 'Fawkes' 'Faces', 'get_file', 'filter_image_paths', 'main', 'Fawkes'
) )

37
fawkes/differentiator.py
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@ -10,8 +10,8 @@ from decimal import Decimal
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
from fawkes.utils import preprocess, reverse_preprocess from fawkes.utils import preprocess, reverse_preprocess
from keras.utils import Progbar
class FawkesMaskGeneration: class FawkesMaskGeneration:
@ -226,8 +226,6 @@ class FawkesMaskGeneration:
self.init = tf.variables_initializer(var_list=[self.modifier] + new_vars) self.init = tf.variables_initializer(var_list=[self.modifier] + new_vars)
print('Attacker loaded')
def preprocess_arctanh(self, imgs): def preprocess_arctanh(self, imgs):
imgs = reverse_preprocess(imgs, self.intensity_range) imgs = reverse_preprocess(imgs, self.intensity_range)
@ -276,7 +274,7 @@ class FawkesMaskGeneration:
adv_imgs.extend(adv_img) adv_imgs.extend(adv_img)
elapsed_time = time.time() - start_time elapsed_time = time.time() - start_time
print('attack cost %f s' % (elapsed_time)) print('protection cost %f s' % (elapsed_time))
return np.array(adv_imgs) return np.array(adv_imgs)
@ -356,7 +354,6 @@ class FawkesMaskGeneration:
bottlesim_sum / nb_imgs)) bottlesim_sum / nb_imgs))
finished_idx = set() finished_idx = set()
try:
total_distance = [0] * nb_imgs total_distance = [0] * nb_imgs
if self.limit_dist: if self.limit_dist:
@ -370,6 +367,11 @@ class FawkesMaskGeneration:
break break
total_distance[e] = bottlesim total_distance[e] = bottlesim
if self.verbose == 0:
progressbar = Progbar(
self.MAX_ITERATIONS, width=30, verbose=1
)
for iteration in range(self.MAX_ITERATIONS): for iteration in range(self.MAX_ITERATIONS):
self.sess.run([self.train], feed_dict={self.learning_rate_holder: LR}) self.sess.run([self.train], feed_dict={self.learning_rate_holder: LR})
@ -407,7 +409,6 @@ class FawkesMaskGeneration:
if iteration != 0 and iteration % (self.MAX_ITERATIONS // 2) == 0: if iteration != 0 and iteration % (self.MAX_ITERATIONS // 2) == 0:
LR = LR * 0.8 LR = LR * 0.8
print("Learning Rate: ", LR)
if iteration % (self.MAX_ITERATIONS // 5) == 0: if iteration % (self.MAX_ITERATIONS // 5) == 0:
if self.verbose == 1: if self.verbose == 1:
@ -415,25 +416,8 @@ class FawkesMaskGeneration:
bottlesim_sum = self.sess.run(self.bottlesim_sum) bottlesim_sum = self.sess.run(self.bottlesim_sum)
print('ITER %4d perturb: %.5f; sim: %f' print('ITER %4d perturb: %.5f; sim: %f'
% (iteration, dist_raw_sum / nb_imgs, bottlesim_sum / nb_imgs)) % (iteration, dist_raw_sum / nb_imgs, bottlesim_sum / nb_imgs))
if self.verbose == 0:
# protected_images = aimg_input_list progressbar.update(iteration)
#
# 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: if self.verbose == 1:
loss_sum = float(self.sess.run(self.loss_sum)) loss_sum = float(self.sess.run(self.loss_sum))
@ -445,7 +429,6 @@ class FawkesMaskGeneration:
dist_sum, dist_sum,
dist_raw_sum, dist_raw_sum,
bottlesim_sum / nb_imgs)) bottlesim_sum / nb_imgs))
print("\n")
best_adv = self.clipping(best_adv[:nb_imgs]) best_adv = self.clipping(best_adv[:nb_imgs])
return best_adv return best_adv

42
fawkes/protection.py
View File

@ -17,15 +17,17 @@ logging.getLogger('tensorflow').disabled = True
import numpy as np import numpy as np
from fawkes.differentiator import FawkesMaskGeneration from fawkes.differentiator import FawkesMaskGeneration
from fawkes.utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, \ from fawkes.utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, \
Faces Faces, filter_image_paths
from fawkes.align_face import aligner from fawkes.align_face import aligner
from fawkes.utils import get_file from fawkes.utils import get_file
random.seed(12243) random.seed(12243)
np.random.seed(122412) np.random.seed(122412)
def generate_cloak_images(sess, feature_extractors, image_X, target_emb=None, th=0.01, faces=None, sd=1e9, lr=2, 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=1): max_step=500, batch_size=1, debug=False):
batch_size = batch_size if len(image_X) > batch_size else len(image_X) batch_size = batch_size if len(image_X) > batch_size else len(image_X)
differentiator = FawkesMaskGeneration(sess, feature_extractors, differentiator = FawkesMaskGeneration(sess, feature_extractors,
@ -36,7 +38,7 @@ def generate_cloak_images(sess, feature_extractors, image_X, target_emb=None, th
learning_rate=lr, learning_rate=lr,
max_iterations=max_step, max_iterations=max_step,
l_threshold=th, l_threshold=th,
verbose=1, maximize=False, keep_final=False, image_shape=image_X.shape[1:], verbose=1 if debug else 0, maximize=False, keep_final=False, image_shape=image_X.shape[1:],
faces=faces) faces=faces)
cloaked_image_X = differentiator.attack(image_X, target_emb) cloaked_image_X = differentiator.attack(image_X, target_emb)
@ -55,13 +57,14 @@ def check_imgs(imgs):
class Fawkes(object): class Fawkes(object):
def __init__(self, feature_extractor, gpu, batch_size): def __init__(self, feature_extractor, gpu, batch_size):
global graph
graph = tf.get_default_graph()
self.feature_extractor = feature_extractor self.feature_extractor = feature_extractor
self.gpu = gpu self.gpu = gpu
self.batch_size = batch_size self.batch_size = batch_size
self.sess = init_gpu(gpu) global sess
sess = init_gpu(gpu)
global graph
graph = tf.get_default_graph()
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes') model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
if not os.path.exists(os.path.join(model_dir, "mtcnn.p.gz")): if not os.path.exists(os.path.join(model_dir, "mtcnn.p.gz")):
@ -69,11 +72,11 @@ class Fawkes(object):
get_file("mtcnn.p.gz", "http://sandlab.cs.uchicago.edu/fawkes/files/mtcnn.p.gz", cache_dir=model_dir, get_file("mtcnn.p.gz", "http://sandlab.cs.uchicago.edu/fawkes/files/mtcnn.p.gz", cache_dir=model_dir,
cache_subdir='') cache_subdir='')
self.aligner = aligner(self.sess)
self.fs_names = [feature_extractor] self.fs_names = [feature_extractor]
if isinstance(feature_extractor, list): if isinstance(feature_extractor, list):
self.fs_names = feature_extractor self.fs_names = feature_extractor
self.aligner = aligner(sess)
self.feature_extractors_ls = [load_extractor(name) for name in self.fs_names] self.feature_extractors_ls = [load_extractor(name) for name in self.fs_names]
def mode2param(self, mode): def mode2param(self, mode):
@ -101,22 +104,26 @@ class Fawkes(object):
return th, max_step, lr return th, max_step, lr
def run_protection(self, image_paths, mode='mid', th=0.04, sd=1e9, lr=10, max_step=500, batch_size=1, format='png', 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): separate_target=True, debug=False):
if mode == 'custom': if mode == 'custom':
pass pass
else: else:
th, max_step, lr = self.mode2param(mode) th, max_step, lr = self.mode2param(mode)
image_paths, loaded_images = filter_image_paths(image_paths)
start_time = time.time() start_time = time.time()
if not image_paths: if not image_paths:
raise Exception("No images in the directory") raise Exception("No images in the directory")
with graph.as_default(): with graph.as_default():
faces = Faces(image_paths, self.aligner, verbose=1) faces = Faces(image_paths, loaded_images, self.aligner, verbose=1)
original_images = faces.cropped_faces original_images = faces.cropped_faces
original_images = np.array(original_images) original_images = np.array(original_images)
with sess.as_default():
if separate_target: if separate_target:
target_embedding = [] target_embedding = []
for org_img in original_images: for org_img in original_images:
@ -127,22 +134,22 @@ class Fawkes(object):
else: else:
target_embedding = select_target_label(original_images, self.feature_extractors_ls, self.fs_names) 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, protected_images = generate_cloak_images(sess, self.feature_extractors_ls, original_images,
target_emb=target_embedding, th=th, faces=faces, sd=sd, target_emb=target_embedding, th=th, faces=faces, sd=sd,
lr=lr, max_step=max_step, batch_size=batch_size) lr=lr, max_step=max_step, batch_size=batch_size, debug=debug)
faces.cloaked_cropped_faces = protected_images faces.cloaked_cropped_faces = protected_images
cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(original_images) cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(
original_images)
final_images = faces.merge_faces(cloak_perturbation) final_images = faces.merge_faces(cloak_perturbation)
for p_img, cloaked_img, path in zip(final_images, protected_images, image_paths): for p_img, path in zip(final_images, image_paths):
file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), mode, format) file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), mode, format)
dump_image(p_img, file_name, format=format) dump_image(p_img, file_name, format=format)
# elapsed_time = time.time() - start_time
elapsed_time = time.time() - start_time
print('attack cost %f s' % elapsed_time)
print("Done!") print("Done!")
return None
def main(*argv): def main(*argv):
@ -175,6 +182,7 @@ def main(*argv):
parser.add_argument('--batch-size', type=int, default=1) parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--separate_target', action='store_true') parser.add_argument('--separate_target', action='store_true')
parser.add_argument('--debug', action='store_true')
parser.add_argument('--format', type=str, parser.add_argument('--format', type=str,
help="final image format", help="final image format",
@ -192,7 +200,7 @@ def main(*argv):
protector = Fawkes(args.feature_extractor, args.gpu, args.batch_size) protector = Fawkes(args.feature_extractor, args.gpu, args.batch_size)
protector.run_protection(image_paths, mode=args.mode, th=args.th, sd=args.sd, lr=args.lr, max_step=args.max_step, protector.run_protection(image_paths, mode=args.mode, th=args.th, sd=args.sd, lr=args.lr, max_step=args.max_step,
batch_size=args.batch_size, format=args.format, batch_size=args.batch_size, format=args.format,
separate_target=args.separate_target) separate_target=args.separate_target, debug=args.debug)
if __name__ == '__main__': if __name__ == '__main__':

44
fawkes/utils.py
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@ -55,6 +55,7 @@ if sys.version_info[0] == 2:
else: else:
from six.moves.urllib.request import urlretrieve from six.moves.urllib.request import urlretrieve
def clip_img(X, preprocessing='raw'): def clip_img(X, preprocessing='raw'):
X = reverse_preprocess(X, preprocessing) X = reverse_preprocess(X, preprocessing)
X = np.clip(X, 0.0, 255.0) X = np.clip(X, 0.0, 255.0)
@ -67,6 +68,8 @@ def load_image(path):
img = Image.open(path) img = Image.open(path)
except PIL.UnidentifiedImageError: except PIL.UnidentifiedImageError:
return None return None
except IsADirectoryError:
return None
if img._getexif() is not None: if img._getexif() is not None:
for orientation in ExifTags.TAGS.keys(): for orientation in ExifTags.TAGS.keys():
@ -89,9 +92,24 @@ def load_image(path):
return image_array return image_array
class Faces(object): def filter_image_paths(image_paths):
def __init__(self, image_paths, aligner, verbose=1, eval_local=False): print("Identify {} files in the directory".format(len(image_paths)))
new_image_paths = []
new_images = []
for p in image_paths:
img = load_image(p)
if img is None:
print("{} is not an image file, skipped".format(p.split("/")[-1]))
continue
new_image_paths.append(p)
new_images.append(img)
print("Identify {} images in the directory".format(len(new_image_paths)))
return new_image_paths, new_images
class Faces(object):
def __init__(self, image_paths, loaded_images, aligner, verbose=1, eval_local=False):
self.image_paths = image_paths
self.verbose = verbose self.verbose = verbose
self.aligner = aligner self.aligner = aligner
self.org_faces = [] self.org_faces = []
@ -99,12 +117,9 @@ class Faces(object):
self.cropped_faces_shape = [] self.cropped_faces_shape = []
self.cropped_index = [] self.cropped_index = []
self.callback_idx = [] self.callback_idx = []
if verbose: for i in range(0, len(loaded_images)):
print("Identify {} images".format(len(image_paths))) cur_img = loaded_images[i]
for i, p in enumerate(image_paths): p = image_paths[i]
cur_img = load_image(p)
if cur_img is None:
continue
self.org_faces.append(cur_img) self.org_faces.append(cur_img)
@ -115,7 +130,7 @@ class Faces(object):
align_img = align(cur_img, self.aligner, margin=margin) align_img = align(cur_img, self.aligner, margin=margin)
if align_img is None: if align_img is None:
print("Find 0 face(s) in {}".format(p.split("/")[-1])) print("Find 0 face(s)".format(p.split("/")[-1]))
continue continue
cur_faces = align_img[0] cur_faces = align_img[0]
@ -143,8 +158,7 @@ class Faces(object):
self.callback_idx.extend([i] * len(cur_faces_square)) self.callback_idx.extend([i] * len(cur_faces_square))
if not self.cropped_faces: if not self.cropped_faces:
print("No faces detected") raise Exception("No faces detected")
exit(1)
self.cropped_faces = np.array(self.cropped_faces) self.cropped_faces = np.array(self.cropped_faces)
@ -469,8 +483,11 @@ def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, m
embs = np.array(embs) embs = np.array(embs)
pair_dist = pairwise_l2_distance(original_feature_x, embs) pair_dist = pairwise_l2_distance(original_feature_x, embs)
pair_dist = np.array(pair_dist)
max_sum = np.min(pair_dist, axis=0) max_sum = np.min(pair_dist, axis=0)
max_id = np.argmax(max_sum) max_id_ls = np.argsort(max_sum)[::-1]
max_id = random.choice(max_id_ls[:20])
target_data_id = paths[int(max_id)] target_data_id = paths[int(max_id)]
image_dir = os.path.join(model_dir, "target_data/{}".format(target_data_id)) image_dir = os.path.join(model_dir, "target_data/{}".format(target_data_id))
@ -480,9 +497,12 @@ def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, m
for i in range(10): for i in range(10):
if os.path.exists(os.path.join(model_dir, "target_data/{}/{}.jpg".format(target_data_id, i))): if os.path.exists(os.path.join(model_dir, "target_data/{}/{}.jpg".format(target_data_id, i))):
continue continue
try:
get_file("{}.jpg".format(i), get_file("{}.jpg".format(i),
"http://sandlab.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg".format(target_data_id, i), "http://sandlab.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg".format(target_data_id, i),
cache_dir=model_dir, cache_subdir='target_data/{}/'.format(target_data_id)) cache_dir=model_dir, cache_subdir='target_data/{}/'.format(target_data_id))
except Exception:
pass
image_paths = glob.glob(image_dir + "/*.jpg") image_paths = glob.glob(image_dir + "/*.jpg")