mirror of
https://github.com/Shawn-Shan/fawkes.git
synced 2024-12-22 07:09:33 +05:30
make it into a class
Former-commit-id: a16d0d213b568eb5cbc21229b940394fc58e1818 [formerly 4bb10aac31590439604bc9750fc51a6003be5b4c] Former-commit-id: 2316435e65d25480560f94dc6a9aff560fe0673e
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Shawn-Shan
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9d176d62b2
commit
f5749d0ae4
@ -4,7 +4,7 @@
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# @Link : https://www.shawnshan.com/
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__version__ = '0.0.5'
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__version__ = '0.0.6'
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from .detect_faces import create_mtcnn, run_detect_face
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from .differentiator import FawkesMaskGeneration
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@ -4,12 +4,14 @@
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import argparse
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import glob
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import logging
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import os
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import random
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import sys
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import time
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import tensorflow as tf
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import logging
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logging.getLogger('tensorflow').disabled = True
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import numpy as np
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@ -17,7 +19,6 @@ from fawkes.differentiator import FawkesMaskGeneration
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from fawkes.utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, \
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Faces
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random.seed(12243)
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np.random.seed(122412)
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@ -51,8 +52,88 @@ def check_imgs(imgs):
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return imgs
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class Fawkes(object):
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def __init__(self, feature_extractor, gpu, batch_size):
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self.feature_extractor = feature_extractor
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self.gpu = gpu
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self.batch_size = batch_size
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self.sess = init_gpu(gpu)
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self.fs_names = [feature_extractor]
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if isinstance(feature_extractor, list):
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self.fs_names = feature_extractor
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self.feature_extractors_ls = [load_extractor(name) for name in self.fs_names]
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def mode2param(self, mode):
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if mode == 'low':
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th = 0.003
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max_step = 20
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lr = 20
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elif mode == 'mid':
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th = 0.005
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max_step = 50
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lr = 15
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elif mode == 'high':
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th = 0.008
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max_step = 500
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lr = 15
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elif mode == 'ultra':
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if not tf.test.is_gpu_available():
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print("Please enable GPU for ultra setting...")
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sys.exit(1)
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th = 0.01
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max_step = 2000
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lr = 8
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else:
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raise Exception("mode must be one of 'low', 'mid', 'high', 'ultra', 'custom'")
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return th, max_step, lr
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def run_protection(self, image_paths, mode='mid', th=0.04, sd=1e9, lr=10, max_step=500, batch_size=1, format='png',
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separate_target=True):
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if mode == 'custom':
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pass
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else:
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th, max_step, lr = self.mode2param(mode)
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start_time = time.time()
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if not image_paths:
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raise Exception("No images in the directory")
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faces = Faces(image_paths, self.sess, verbose=1)
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orginal_images = faces.cropped_faces
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orginal_images = np.array(orginal_images)
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if separate_target:
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target_embedding = []
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for org_img in orginal_images:
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org_img = org_img.reshape([1] + list(org_img.shape))
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tar_emb = select_target_label(org_img, self.feature_extractors_ls, self.fs_names)
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target_embedding.append(tar_emb)
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target_embedding = np.concatenate(target_embedding)
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else:
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target_embedding = select_target_label(orginal_images, self.feature_extractors_ls, self.fs_names)
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protected_images = generate_cloak_images(self.sess, self.feature_extractors_ls, orginal_images,
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target_emb=target_embedding, th=th, faces=faces, sd=sd,
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lr=lr, max_step=max_step, batch_size=batch_size)
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faces.cloaked_cropped_faces = protected_images
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cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(orginal_images)
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final_images = faces.merge_faces(cloak_perturbation)
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for p_img, cloaked_img, path in zip(final_images, protected_images, image_paths):
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file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), mode, format)
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dump_image(p_img, file_name, format=format)
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elapsed_time = time.time() - start_time
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print('attack cost %f s' % (elapsed_time))
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print("Done!")
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def main(*argv):
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start_time = time.time()
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if not argv:
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argv = list(sys.argv)
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@ -86,85 +167,20 @@ def main(*argv):
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parser.add_argument('--format', type=str,
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help="final image format",
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default="png")
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args = parser.parse_args(argv[1:])
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if args.mode == 'low':
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args.feature_extractor = "high_extract"
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args.th = 0.003
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args.max_step = 20
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args.lr = 20
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elif args.mode == 'mid':
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args.feature_extractor = "high_extract"
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args.th = 0.005
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args.max_step = 50
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args.lr = 15
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elif args.mode == 'high':
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args.feature_extractor = "high_extract"
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args.th = 0.008
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args.max_step = 500
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args.lr = 15
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elif args.mode == 'ultra':
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if not tf.test.is_gpu_available():
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print("Please enable GPU for ultra setting...")
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sys.exit(1)
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# args.feature_extractor = ["high_extract", 'high2_extract']
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args.feature_extractor = "high_extract"
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args.th = 0.01
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args.max_step = 2000
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args.lr = 8
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elif args.mode == 'custom':
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pass
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else:
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raise Exception("mode must be one of 'low', 'mid', 'high', 'ultra', 'custom'")
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args = parser.parse_args(argv[1:])
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assert args.format in ['png', 'jpg', 'jpeg']
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if args.format == 'jpg':
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args.format = 'jpeg'
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sess = init_gpu(args.gpu)
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image_paths = glob.glob(os.path.join(args.directory, "*"))
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image_paths = [path for path in image_paths if "_cloaked" not in path.split("/")[-1]]
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if not image_paths:
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raise Exception("No images in the directory")
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faces = Faces(image_paths, sess, verbose=1)
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orginal_images = faces.cropped_faces
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orginal_images = np.array(orginal_images)
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fs_names = [args.feature_extractor]
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if isinstance(args.feature_extractor, list):
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fs_names = args.feature_extractor
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feature_extractors_ls = [load_extractor(name) for name in fs_names]
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if args.separate_target:
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target_embedding = []
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for org_img in orginal_images:
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org_img = org_img.reshape([1] + list(org_img.shape))
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tar_emb = select_target_label(org_img, feature_extractors_ls, fs_names)
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target_embedding.append(tar_emb)
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target_embedding = np.concatenate(target_embedding)
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else:
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target_embedding = select_target_label(orginal_images, feature_extractors_ls, fs_names)
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protected_images = generate_cloak_images(sess, feature_extractors_ls, orginal_images,
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target_emb=target_embedding, th=args.th, faces=faces, sd=args.sd,
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lr=args.lr, max_step=args.max_step, batch_size=args.batch_size)
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faces.cloaked_cropped_faces = protected_images
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cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(orginal_images)
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final_images = faces.merge_faces(cloak_perturbation)
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for p_img, cloaked_img, path in zip(final_images, protected_images, image_paths):
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file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), args.mode, args.format)
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dump_image(p_img, file_name, format=args.format)
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elapsed_time = time.time() - start_time
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print('attack cost %f s' % (elapsed_time))
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print("Done!")
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protector = Fawkes(args.feature_extractor, args.gpu, args.batch_size)
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protector.run_protection(image_paths, mode=args.mode, th=args.th, sd=args.sd, lr=args.lr, max_step=args.max_step,
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batch_size=args.batch_size, format=args.format,
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separate_target=args.separate_target)
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if __name__ == '__main__':
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