kopia lustrzana https://github.com/Shawn-Shan/fawkes
96 wiersze
3.7 KiB
Python
96 wiersze
3.7 KiB
Python
import argparse
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import os
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import pickle
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import random
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import sys
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import numpy as np
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from differentiator import FawkesMaskGeneration
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from tensorflow import set_random_seed
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from utils import load_extractor, CloakData, init_gpu
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random.seed(12243)
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np.random.seed(122412)
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set_random_seed(12242)
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NUM_IMG_PROTECTED = 400 # Number of images used to optimize the target class
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BATCH_SIZE = 32
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MAX_ITER = 1000
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def diff_protected_data(sess, feature_extractors_ls, image_X, number_protect, target_X=None, th=0.01):
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image_X = image_X[:number_protect]
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differentiator = FawkesMaskGeneration(sess, feature_extractors_ls,
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batch_size=BATCH_SIZE,
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mimic_img=True,
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intensity_range='imagenet',
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initial_const=args.sd,
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learning_rate=args.lr,
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max_iterations=MAX_ITER,
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l_threshold=th,
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verbose=1, maximize=False, keep_final=False, image_shape=image_X.shape[1:])
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if len(target_X) < len(image_X):
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target_X = np.concatenate([target_X, target_X, target_X])
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target_X = target_X[:len(image_X)]
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cloaked_image_X = differentiator.attack(image_X, target_X)
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return cloaked_image_X
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def perform_defense():
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RES = {}
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sess = init_gpu(args.gpu)
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FEATURE_EXTRACTORS = [args.feature_extractor]
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RES_DIR = '../results/'
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RES['num_img_protected'] = NUM_IMG_PROTECTED
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RES['extractors'] = FEATURE_EXTRACTORS
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num_protect = NUM_IMG_PROTECTED
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print("Loading {} for optimization".format(args.feature_extractor))
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feature_extractors_ls = [load_extractor(name) for name in FEATURE_EXTRACTORS]
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protect_class = args.protect_class
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cloak_data = CloakData(args.dataset, protect_class=protect_class)
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RES_FILE_NAME = "{}_{}_protect{}".format(args.dataset, args.feature_extractor, cloak_data.protect_class)
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RES_FILE_NAME = os.path.join(RES_DIR, RES_FILE_NAME)
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print("Protect Class: ", cloak_data.protect_class)
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cloak_data.target_path, cloak_data.target_data = cloak_data.select_target_label(feature_extractors_ls,
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FEATURE_EXTRACTORS)
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os.makedirs(RES_DIR, exist_ok=True)
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os.makedirs(RES_FILE_NAME, exist_ok=True)
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cloak_image_X = diff_protected_data(sess, feature_extractors_ls, cloak_data.protect_train_X,
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number_protect=num_protect,
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target_X=cloak_data.target_data, th=args.th)
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cloak_data.cloaked_protect_train_X = cloak_image_X
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RES['cloak_data'] = cloak_data
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pickle.dump(RES, open(os.path.join(RES_FILE_NAME, 'cloak_data.p'), "wb"))
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def parse_arguments(argv):
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parser = argparse.ArgumentParser()
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parser.add_argument('--gpu', type=str,
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help='GPU id', default='0')
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parser.add_argument('--dataset', type=str,
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help='name of dataset', default='scrub')
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parser.add_argument('--feature-extractor', type=str,
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help="name of the feature extractor used for optimization",
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default="webface_dense_robust_extract")
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parser.add_argument('--th', type=float, default=0.007)
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parser.add_argument('--sd', type=int, default=1e9)
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parser.add_argument('--protect_class', type=str, default=None)
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parser.add_argument('--lr', type=float, default=1)
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return parser.parse_args(argv)
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if __name__ == '__main__':
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args = parse_arguments(sys.argv[1:])
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perform_defense()
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