ResearchPapers/fawkes
ResearchPapers/fawkes
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make it into a class

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Former-commit-id: a16d0d213b568eb5cbc21229b940394fc58e1818 [formerly 4bb10aac31590439604bc9750fc51a6003be5b4c]
Former-commit-id: 2316435e65d25480560f94dc6a9aff560fe0673e
This commit is contained in:
Shawn-Shan 2020-07-09 01:26:42 -05:00
parent 9d176d62b2
commit f5749d0ae4
2 changed files with 90 additions and 74 deletions
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2
fawkes/__init__.py
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@ -4,7 +4,7 @@
# @Link : https://www.shawnshan.com/ # @Link : https://www.shawnshan.com/
__version__ = '0.0.5' __version__ = '0.0.6'
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

162
fawkes/protection.py
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@ -4,12 +4,14 @@
import argparse import argparse
import glob import glob
import logging
import os import os
import random import random
import sys import sys
import time import time
import tensorflow as tf import tensorflow as tf
import logging
logging.getLogger('tensorflow').disabled = True logging.getLogger('tensorflow').disabled = True
import numpy as np import numpy as np
@ -17,7 +19,6 @@ 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
random.seed(12243) random.seed(12243)
np.random.seed(122412) np.random.seed(122412)
@ -51,8 +52,88 @@ def check_imgs(imgs):
return imgs return imgs
def main(*argv): class Fawkes(object):
def __init__(self, feature_extractor, gpu, batch_size):
self.feature_extractor = feature_extractor
self.gpu = gpu
self.batch_size = batch_size
self.sess = init_gpu(gpu)
self.fs_names = [feature_extractor]
if isinstance(feature_extractor, list):
self.fs_names = feature_extractor
self.feature_extractors_ls = [load_extractor(name) for name in self.fs_names]
def mode2param(self, mode):
if mode == 'low':
th = 0.003
max_step = 20
lr = 20
elif mode == 'mid':
th = 0.005
max_step = 50
lr = 15
elif mode == 'high':
th = 0.008
max_step = 500
lr = 15
elif mode == 'ultra':
if not tf.test.is_gpu_available():
print("Please enable GPU for ultra setting...")
sys.exit(1)
th = 0.01
max_step = 2000
lr = 8
else:
raise Exception("mode must be one of 'low', 'mid', 'high', 'ultra', 'custom'")
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',
separate_target=True):
if mode == 'custom':
pass
else:
th, max_step, lr = self.mode2param(mode)
start_time = time.time() start_time = time.time()
if not image_paths:
raise Exception("No images in the directory")
faces = Faces(image_paths, self.sess, verbose=1)
orginal_images = faces.cropped_faces
orginal_images = np.array(orginal_images)
if 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, self.feature_extractors_ls, self.fs_names)
target_embedding.append(tar_emb)
target_embedding = np.concatenate(target_embedding)
else:
target_embedding = select_target_label(orginal_images, self.feature_extractors_ls, self.fs_names)
protected_images = generate_cloak_images(self.sess, self.feature_extractors_ls, orginal_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(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]), 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 main(*argv):
if not argv: if not argv:
argv = list(sys.argv) argv = list(sys.argv)
@ -86,85 +167,20 @@ def main(*argv):
parser.add_argument('--format', type=str, parser.add_argument('--format', type=str,
help="final image format", help="final image format",
default="png") default="png")
args = parser.parse_args(argv[1:])
if args.mode == 'low': args = parser.parse_args(argv[1:])
args.feature_extractor = "high_extract"
args.th = 0.003
args.max_step = 20
args.lr = 20
elif args.mode == 'mid':
args.feature_extractor = "high_extract"
args.th = 0.005
args.max_step = 50
args.lr = 15
elif args.mode == 'high':
args.feature_extractor = "high_extract"
args.th = 0.008
args.max_step = 500
args.lr = 15
elif args.mode == 'ultra':
if not tf.test.is_gpu_available():
print("Please enable GPU for ultra setting...")
sys.exit(1)
# args.feature_extractor = ["high_extract", 'high2_extract']
args.feature_extractor = "high_extract"
args.th = 0.01
args.max_step = 2000
args.lr = 8
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'] assert args.format in ['png', 'jpg', 'jpeg']
if args.format == 'jpg': if args.format == 'jpg':
args.format = 'jpeg' args.format = 'jpeg'
sess = init_gpu(args.gpu)
image_paths = glob.glob(os.path.join(args.directory, "*")) image_paths = glob.glob(os.path.join(args.directory, "*"))
image_paths = [path for path in image_paths if "_cloaked" not in path.split("/")[-1]] image_paths = [path for path in image_paths if "_cloaked" not in path.split("/")[-1]]
if not image_paths:
raise Exception("No images in the directory")
faces = Faces(image_paths, sess, verbose=1) 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,
orginal_images = faces.cropped_faces batch_size=args.batch_size, format=args.format,
orginal_images = np.array(orginal_images) separate_target=args.separate_target)
fs_names = [args.feature_extractor]
if isinstance(args.feature_extractor, list):
fs_names = args.feature_extractor
feature_extractors_ls = [load_extractor(name) for name in fs_names]
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, batch_size=args.batch_size)
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.format)
dump_image(p_img, file_name, format=args.format)
elapsed_time = time.time() - start_time
print('attack cost %f s' % (elapsed_time))
print("Done!")
if __name__ == '__main__': if __name__ == '__main__':