kopia lustrzana https://github.com/Shawn-Shan/fawkes
add fawkes-lite and fawkes-dev
Shawn-Shan
rodzic
5b15437b69
commit
7fc3a82437
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@ -136,18 +136,21 @@ class FawkesMaskGeneration:
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def batch_gen_DSSIM(aimg_raw_split, simg_raw_split):
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msssim_split = tf.image.ssim(aimg_raw_split, simg_raw_split, max_val=255.0)
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dist = (1.0 - tf.stack(msssim_split)) / 2.0
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# dist = tf.square(aimg_raw_split - simg_raw_split)
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return dist
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# raw value of DSSIM distance
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self.dist_raw = batch_gen_DSSIM(self.aimg_raw, self.simg_raw)
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# distance value after applying threshold
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self.dist = tf.maximum(self.dist_raw - self.l_threshold, 0.0)
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# self.dist = self.dist_raw
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self.dist_raw_sum = tf.reduce_sum(
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tf.where(self.mask,
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self.dist_raw,
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tf.zeros_like(self.dist_raw)))
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self.dist_sum = tf.reduce_sum(tf.where(self.mask, self.dist, tf.zeros_like(self.dist)))
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# self.dist_sum = 1e-5 * tf.reduce_sum(self.dist)
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# self.dist_raw_sum = self.dist_sum
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def resize_tensor(input_tensor, model_input_shape):
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if input_tensor.shape[1:] == model_input_shape or model_input_shape[1] is None:
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@ -158,12 +161,6 @@ class FawkesMaskGeneration:
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def calculate_direction(bottleneck_model, cur_timg_input, cur_simg_input):
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target_features = bottleneck_model(cur_timg_input)
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return target_features
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# target_center = tf.reduce_mean(target_features, axis=0)
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# original = bottleneck_model(cur_simg_input)
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# original_center = tf.reduce_mean(original, axis=0)
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# direction = target_center - original_center
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# final_target = original + self.ratio * direction
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# return final_target
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self.bottlesim = 0.0
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self.bottlesim_sum = 0.0
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@ -201,7 +198,14 @@ class FawkesMaskGeneration:
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else:
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self.loss = self.const * tf.square(self.dist) + self.bottlesim
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self.loss_sum = tf.reduce_sum(tf.where(self.mask, self.loss, tf.zeros_like(self.loss)))
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self.loss_sum = tf.reduce_sum(tf.where(self.mask,
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self.loss,
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tf.zeros_like(self.loss)))
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# self.loss_sum = self.dist_sum + tf.reduce_sum(self.bottlesim)
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# import pdb
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# pdb.set_trace()
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# self.loss_sum = tf.reduce_sum(tf.where(self.mask, self.loss, tf.zeros_like(self.loss)))
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# Setup the Adadelta optimizer and keep track of variables
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# we're creating
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@ -321,25 +325,13 @@ class FawkesMaskGeneration:
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weights_batch[:nb_imgs] = weights[:nb_imgs]
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modifier_batch = np.ones(self.input_shape) * 1e-6
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# set the variables so that we don't have to send them over again
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if self.MIMIC_IMG:
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self.sess.run(self.setup,
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{self.assign_timg_tanh: timg_tanh_batch,
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self.assign_simg_tanh: simg_tanh_batch,
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self.assign_const: CONST,
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self.assign_mask: mask,
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self.assign_weights: weights_batch,
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self.assign_modifier: modifier_batch})
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else:
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# if directly mimicking a vector, use assign_bottleneck_t_raw
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# in setup
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self.sess.run(self.setup,
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{self.assign_bottleneck_t_raw: timg_tanh_batch,
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self.assign_simg_tanh: simg_tanh_batch,
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self.assign_const: CONST,
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self.assign_mask: mask,
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self.assign_weights: weights_batch,
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self.assign_modifier: modifier_batch})
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self.sess.run(self.setup,
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{self.assign_timg_tanh: timg_tanh_batch,
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self.assign_simg_tanh: simg_tanh_batch,
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self.assign_const: CONST,
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self.assign_mask: mask,
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self.assign_weights: weights_batch,
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self.assign_modifier: modifier_batch})
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best_bottlesim = [0] * nb_imgs if self.maximize else [np.inf] * nb_imgs
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best_adv = np.zeros_like(source_imgs)
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@ -390,7 +382,7 @@ class FawkesMaskGeneration:
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best_adv[e] = aimg_input
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if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0:
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LR = LR / 2
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# LR = LR / 2
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print("Learning Rate: ", LR)
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if iteration % (self.MAX_ITERATIONS // 10) == 0:
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@ -1,28 +1,29 @@
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import argparse
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import glob
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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 keras.applications.vgg16 import preprocess_input
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from keras.preprocessing import image
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from skimage.transform import resize
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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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from utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked
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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 = 32 # 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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def generate_cloak_images(sess, feature_extractors, image_X, target_X=None, th=0.01):
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batch_size = BATCH_SIZE if len(image_X) > BATCH_SIZE else len(image_X)
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differentiator = FawkesMaskGeneration(sess, feature_extractors,
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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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@ -31,65 +32,81 @@ def diff_protected_data(sess, feature_extractors_ls, image_X, number_protect, ta
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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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def extract_faces(img):
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# foo
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return preprocess_input(resize(img, (224, 224)))
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def fawkes():
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assert os.path.exists(args.directory)
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assert os.path.isdir(args.directory)
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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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feature_extractors_ls = [load_extractor(args.feature_extractor)]
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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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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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os.makedirs(RES_DIR, exist_ok=True)
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os.makedirs(RES_FILE_NAME, exist_ok=True)
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orginal_images = [extract_faces(image.img_to_array(image.load_img(cur_path))) for cur_path in
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image_paths]
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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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orginal_images = np.array(orginal_images)
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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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if args.seperate_target:
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target_images = []
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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_img = select_target_label(org_img, feature_extractors_ls, [args.feature_extractor])
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target_images.append(tar_img)
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target_images = np.concatenate(target_images)
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# import pdb
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# pdb.set_trace()
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else:
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target_images = select_target_label(orginal_images, feature_extractors_ls, [args.feature_extractor])
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# file_name = args.directory.split("/")[-1]
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# os.makedirs(args.result_directory, exist_ok=True)
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# os.makedirs(os.path.join(args.result_directory, file_name), exist_ok=True)
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protected_images = generate_cloak_images(sess, feature_extractors_ls, orginal_images,
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target_X=target_images, th=args.th)
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for p_img, path in zip(protected_images, image_paths):
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p_img = reverse_process_cloaked(p_img)
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# img_type = path.split(".")[-1]
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file_name = "{}_cloaked.jpeg".format(".".join(path.split(".")[:-1]))
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dump_image(p_img, file_name, format="JPEG")
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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('--directory', type=str,
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help='directory that contain images for cloaking', default='imgs/')
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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=1e5)
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parser.add_argument('--th', type=float, default=0.005)
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parser.add_argument('--sd', type=int, default=1e10)
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parser.add_argument('--protect_class', type=str, default=None)
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parser.add_argument('--lr', type=float, default=0.1)
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parser.add_argument('--result_directory', type=str, default="../results")
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parser.add_argument('--seperate_target', action='store_true')
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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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fawkes()
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120
fawkes/utils.py
120
fawkes/utils.py
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@ -141,7 +141,6 @@ def imagenet_preprocessing(x, data_format=None):
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return x
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def imagenet_reverse_preprocessing(x, data_format=None):
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import keras.backend as K
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x = np.array(x)
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@ -172,6 +171,11 @@ def imagenet_reverse_preprocessing(x, data_format=None):
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return x
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def reverse_process_cloaked(x, preprocess='imagenet'):
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x = clip_img(x, preprocess)
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return reverse_preprocess(x, preprocess)
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def build_bottleneck_model(model, cut_off):
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bottleneck_model = Model(model.input, model.get_layer(cut_off).output)
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bottleneck_model.compile(loss='categorical_crossentropy',
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@ -212,32 +216,116 @@ def normalize(x):
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return x / np.linalg.norm(x, axis=1, keepdims=True)
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def dump_image(x, filename, format="png", scale=False):
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img = image.array_to_img(x, scale=scale)
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img.save(filename, format)
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return
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def load_dir(path):
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assert os.path.exists(path)
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x_ls = []
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for file in os.listdir(path):
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cur_path = os.path.join(path, file)
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im = image.load_img(cur_path, target_size=(224, 224))
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im = image.img_to_array(im)
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x_ls.append(im)
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raw_x = np.array(x_ls)
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return preprocess_input(raw_x)
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def load_embeddings(feature_extractors_names):
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dictionaries = []
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for extractor_name in feature_extractors_names:
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path2emb = pickle.load(open("../feature_extractors/embeddings/{}_emb_norm.p".format(extractor_name), "rb"))
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dictionaries.append(path2emb)
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merge_dict = {}
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for k in dictionaries[0].keys():
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cur_emb = [dic[k] for dic in dictionaries]
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merge_dict[k] = np.concatenate(cur_emb)
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return merge_dict
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def extractor_ls_predict(feature_extractors_ls, X):
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feature_ls = []
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for extractor in feature_extractors_ls:
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cur_features = extractor.predict(X)
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feature_ls.append(cur_features)
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concated_feature_ls = np.concatenate(feature_ls, axis=1)
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concated_feature_ls = normalize(concated_feature_ls)
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return concated_feature_ls
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def calculate_dist_score(a, b, feature_extractors_ls, metric='l2'):
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features1 = extractor_ls_predict(feature_extractors_ls, a)
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features2 = extractor_ls_predict(feature_extractors_ls, b)
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pair_cos = pairwise_distances(features1, features2, metric)
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max_sum = np.min(pair_cos, axis=0)
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max_sum_arg = np.argsort(max_sum)[::-1]
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max_sum_arg = max_sum_arg[:len(a)]
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max_sum = [max_sum[i] for i in max_sum_arg]
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paired_target_X = [b[j] for j in max_sum_arg]
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paired_target_X = np.array(paired_target_X)
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return np.min(max_sum), paired_target_X
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def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, metric='l2'):
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original_feature_x = extractor_ls_predict(feature_extractors_ls, imgs)
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path2emb = load_embeddings(feature_extractors_names)
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items = list(path2emb.items())
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paths = [p[0] for p in items]
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embs = [p[1] for p in items]
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embs = np.array(embs)
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pair_dist = pairwise_distances(original_feature_x, embs, metric)
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max_sum = np.min(pair_dist, axis=0)
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sorted_idx = np.argsort(max_sum)[::-1]
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highest_num = 0
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paired_target_X = None
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final_target_class_path = None
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for idx in sorted_idx[:1]:
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target_class_path = paths[idx]
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cur_target_X = load_dir(target_class_path)
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cur_target_X = np.concatenate([cur_target_X, cur_target_X, cur_target_X])
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cur_tot_sum, cur_paired_target_X = calculate_dist_score(imgs, cur_target_X,
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feature_extractors_ls,
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metric=metric)
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if cur_tot_sum > highest_num:
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highest_num = cur_tot_sum
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paired_target_X = cur_paired_target_X
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final_target_class_path = target_class_path
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np.random.shuffle(paired_target_X)
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paired_target_X = list(paired_target_X)
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while len(paired_target_X) < len(imgs):
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paired_target_X += paired_target_X
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paired_target_X = paired_target_X[:len(imgs)]
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return np.array(paired_target_X)
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class CloakData(object):
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def __init__(self, dataset, img_shape=(224, 224), protect_class=None):
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self.dataset = dataset
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def __init__(self, protect_directory=None, img_shape=(224, 224)):
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self.img_shape = img_shape
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self.train_data_dir, self.test_data_dir, self.number_classes, self.number_samples = get_dataset_path(dataset)
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self.all_labels = sorted(list(os.listdir(self.train_data_dir)))
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if protect_class:
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self.protect_class = protect_class
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else:
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self.protect_class = random.choice(self.all_labels)
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# self.train_data_dir, self.test_data_dir, self.number_classes, self.number_samples = get_dataset_path(dataset)
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# self.all_labels = sorted(list(os.listdir(self.train_data_dir)))
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self.protect_directory = protect_directory
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self.sybil_class = random.choice([l for l in self.all_labels if l != self.protect_class])
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self.protect_train_X, self.protect_test_X = self.load_label_data(self.protect_class)
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self.sybil_train_X, self.sybil_test_X = self.load_label_data(self.sybil_class)
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self.protect_X = self.load_label_data(self.protect_directory)
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self.cloaked_protect_train_X = None
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self.cloaked_sybil_train_X = None
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self.label2path_train, self.label2path_test, self.path2idx = self.build_data_mapping()
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self.all_training_path = self.get_all_data_path(self.label2path_train)
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self.all_test_path = self.get_all_data_path(self.label2path_test)
|
||||
self.protect_class_path = self.get_class_image_files(os.path.join(self.train_data_dir, self.protect_class))
|
||||
self.sybil_class_path = self.get_class_image_files(os.path.join(self.train_data_dir, self.sybil_class))
|
||||
|
||||
print("Find {} protect images".format(len(self.protect_class_path)))
|
||||
|
||||
def get_class_image_files(self, path):
|
||||
return [os.path.join(path, f) for f in os.listdir(path)]
|
||||
|
|
|
|||
|
|
@ -43,4 +43,15 @@ The code will output a directory in `results/` with `cloak_data.p` inside. You c
|
|||
#### Evaluate Cloak Effectiveness
|
||||
To evaluate the cloak, run `python3 fawkes/eval_cloak.py --gpu 0 --cloak_data PATH-TO-RESULT-DIRECTORY --transfer_model vggface2_inception_extract`.
|
||||
|
||||
The code will print out the tracker model accuracy on uncloaked/original test images of the protected user, which should be close to 0.
|
||||
The code will print out the tracker model accuracy on uncloaked/original test images of the protected user, which should be close to 0.
|
||||
|
||||
|
||||
### Citation
|
||||
```
|
||||
@inproceedings{shan2020fawkes,
|
||||
title={Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models},
|
||||
author={Shan, Shawn and Wenger, Emily and Zhang, Jiayun and Li, Huiying and Zheng, Haitao and Zhao, Ben Y},
|
||||
booktitle="Proc. of USENIX Security",
|
||||
year={2020}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,431 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Date : 2020-05-17
|
||||
# @Author : Shawn Shan (shansixiong@cs.uchicago.edu)
|
||||
# @Link : https://www.shawnshan.com/
|
||||
|
||||
import datetime
|
||||
import time
|
||||
from decimal import Decimal
|
||||
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from utils import preprocess, reverse_preprocess
|
||||
|
||||
|
||||
class FawkesMaskGeneration:
|
||||
# if the attack is trying to mimic a target image or a neuron vector
|
||||
MIMIC_IMG = True
|
||||
# number of iterations to perform gradient descent
|
||||
MAX_ITERATIONS = 10000
|
||||
# larger values converge faster to less accurate results
|
||||
LEARNING_RATE = 1e-2
|
||||
# the initial constant c to pick as a first guess
|
||||
INITIAL_CONST = 1
|
||||
# pixel intensity range
|
||||
INTENSITY_RANGE = 'imagenet'
|
||||
# threshold for distance
|
||||
L_THRESHOLD = 0.03
|
||||
# whether keep the final result or the best result
|
||||
KEEP_FINAL = False
|
||||
# max_val of image
|
||||
MAX_VAL = 255
|
||||
# The following variables are used by DSSIM, should keep as default
|
||||
# filter size in SSIM
|
||||
FILTER_SIZE = 11
|
||||
# filter sigma in SSIM
|
||||
FILTER_SIGMA = 1.5
|
||||
# weights used in MS-SSIM
|
||||
SCALE_WEIGHTS = None
|
||||
MAXIMIZE = False
|
||||
IMAGE_SHAPE = (224, 224, 3)
|
||||
RATIO = 1.0
|
||||
LIMIT_DIST = False
|
||||
|
||||
def __init__(self, sess, bottleneck_model_ls, mimic_img=MIMIC_IMG,
|
||||
batch_size=1, learning_rate=LEARNING_RATE,
|
||||
max_iterations=MAX_ITERATIONS, initial_const=INITIAL_CONST,
|
||||
intensity_range=INTENSITY_RANGE, l_threshold=L_THRESHOLD,
|
||||
max_val=MAX_VAL, keep_final=KEEP_FINAL, maximize=MAXIMIZE, image_shape=IMAGE_SHAPE,
|
||||
verbose=0, ratio=RATIO, limit_dist=LIMIT_DIST):
|
||||
|
||||
assert intensity_range in {'raw', 'imagenet', 'inception', 'mnist'}
|
||||
|
||||
# constant used for tanh transformation to avoid corner cases
|
||||
self.tanh_constant = 2 - 1e-6
|
||||
self.sess = sess
|
||||
self.MIMIC_IMG = mimic_img
|
||||
self.LEARNING_RATE = learning_rate
|
||||
self.MAX_ITERATIONS = max_iterations
|
||||
self.initial_const = initial_const
|
||||
self.batch_size = batch_size
|
||||
self.intensity_range = intensity_range
|
||||
self.l_threshold = l_threshold
|
||||
self.max_val = max_val
|
||||
self.keep_final = keep_final
|
||||
self.verbose = verbose
|
||||
self.maximize = maximize
|
||||
self.learning_rate = learning_rate
|
||||
self.ratio = ratio
|
||||
self.limit_dist = limit_dist
|
||||
self.single_shape = list(image_shape)
|
||||
|
||||
self.input_shape = tuple([self.batch_size] + self.single_shape)
|
||||
|
||||
self.bottleneck_shape = tuple([self.batch_size] + self.single_shape)
|
||||
|
||||
# the variable we're going to optimize over
|
||||
self.modifier = tf.Variable(np.zeros(self.input_shape, dtype=np.float32))
|
||||
|
||||
# target image in tanh space
|
||||
if self.MIMIC_IMG:
|
||||
self.timg_tanh = tf.Variable(np.zeros(self.input_shape), dtype=np.float32)
|
||||
else:
|
||||
self.bottleneck_t_raw = tf.Variable(np.zeros(self.bottleneck_shape), dtype=np.float32)
|
||||
# source image in tanh space
|
||||
self.simg_tanh = tf.Variable(np.zeros(self.input_shape), dtype=np.float32)
|
||||
|
||||
self.const = tf.Variable(np.ones(batch_size), dtype=np.float32)
|
||||
self.mask = tf.Variable(np.ones((batch_size), dtype=np.bool))
|
||||
self.weights = tf.Variable(np.ones(self.bottleneck_shape,
|
||||
dtype=np.float32))
|
||||
|
||||
# and here's what we use to assign them
|
||||
self.assign_modifier = tf.placeholder(tf.float32, self.input_shape)
|
||||
if self.MIMIC_IMG:
|
||||
self.assign_timg_tanh = tf.placeholder(
|
||||
tf.float32, self.input_shape)
|
||||
else:
|
||||
self.assign_bottleneck_t_raw = tf.placeholder(
|
||||
tf.float32, self.bottleneck_shape)
|
||||
self.assign_simg_tanh = tf.placeholder(tf.float32, self.input_shape)
|
||||
self.assign_const = tf.placeholder(tf.float32, (batch_size))
|
||||
self.assign_mask = tf.placeholder(tf.bool, (batch_size))
|
||||
self.assign_weights = tf.placeholder(tf.float32, self.bottleneck_shape)
|
||||
|
||||
# the resulting image, tanh'd to keep bounded from -0.5 to 0.5
|
||||
# adversarial image in raw space
|
||||
self.aimg_raw = (tf.tanh(self.modifier + self.simg_tanh) /
|
||||
self.tanh_constant +
|
||||
0.5) * 255.0
|
||||
# source image in raw space
|
||||
self.simg_raw = (tf.tanh(self.simg_tanh) /
|
||||
self.tanh_constant +
|
||||
0.5) * 255.0
|
||||
if self.MIMIC_IMG:
|
||||
# target image in raw space
|
||||
self.timg_raw = (tf.tanh(self.timg_tanh) /
|
||||
self.tanh_constant +
|
||||
0.5) * 255.0
|
||||
|
||||
# convert source and adversarial image into input space
|
||||
if self.intensity_range == 'imagenet':
|
||||
mean = tf.constant(np.repeat([[[[103.939, 116.779, 123.68]]]], self.batch_size, axis=0), dtype=tf.float32,
|
||||
name='img_mean')
|
||||
self.aimg_input = (self.aimg_raw[..., ::-1] - mean)
|
||||
self.simg_input = (self.simg_raw[..., ::-1] - mean)
|
||||
if self.MIMIC_IMG:
|
||||
self.timg_input = (self.timg_raw[..., ::-1] - mean)
|
||||
|
||||
elif self.intensity_range == 'raw':
|
||||
self.aimg_input = self.aimg_raw
|
||||
self.simg_input = self.simg_raw
|
||||
if self.MIMIC_IMG:
|
||||
self.timg_input = self.timg_raw
|
||||
|
||||
def batch_gen_DSSIM(aimg_raw_split, simg_raw_split):
|
||||
msssim_split = tf.image.ssim(aimg_raw_split, simg_raw_split, max_val=255.0)
|
||||
dist = (1.0 - tf.stack(msssim_split)) / 2.0
|
||||
return dist
|
||||
|
||||
# raw value of DSSIM distance
|
||||
self.dist_raw = batch_gen_DSSIM(self.aimg_raw, self.simg_raw)
|
||||
# distance value after applying threshold
|
||||
self.dist = tf.maximum(self.dist_raw - self.l_threshold, 0.0)
|
||||
|
||||
self.dist_raw_sum = tf.reduce_sum(
|
||||
tf.where(self.mask,
|
||||
self.dist_raw,
|
||||
tf.zeros_like(self.dist_raw)))
|
||||
self.dist_sum = tf.reduce_sum(tf.where(self.mask, self.dist, tf.zeros_like(self.dist)))
|
||||
|
||||
def resize_tensor(input_tensor, model_input_shape):
|
||||
if input_tensor.shape[1:] == model_input_shape or model_input_shape[1] is None:
|
||||
return input_tensor
|
||||
resized_tensor = tf.image.resize(input_tensor, model_input_shape[:2])
|
||||
return resized_tensor
|
||||
|
||||
def calculate_direction(bottleneck_model, cur_timg_input, cur_simg_input):
|
||||
target_features = bottleneck_model(cur_timg_input)
|
||||
return target_features
|
||||
# target_center = tf.reduce_mean(target_features, axis=0)
|
||||
# original = bottleneck_model(cur_simg_input)
|
||||
# original_center = tf.reduce_mean(original, axis=0)
|
||||
# direction = target_center - original_center
|
||||
# final_target = original + self.ratio * direction
|
||||
# return final_target
|
||||
|
||||
self.bottlesim = 0.0
|
||||
self.bottlesim_sum = 0.0
|
||||
self.bottlesim_push = 0.0
|
||||
for bottleneck_model in bottleneck_model_ls:
|
||||
model_input_shape = bottleneck_model.input_shape[1:]
|
||||
cur_aimg_input = resize_tensor(self.aimg_input, model_input_shape)
|
||||
|
||||
self.bottleneck_a = bottleneck_model(cur_aimg_input)
|
||||
if self.MIMIC_IMG:
|
||||
# cur_timg_input = resize_tensor(self.timg_input, model_input_shape)
|
||||
# cur_simg_input = resize_tensor(self.simg_input, model_input_shape)
|
||||
cur_timg_input = self.timg_input
|
||||
cur_simg_input = self.simg_input
|
||||
self.bottleneck_t = calculate_direction(bottleneck_model, cur_timg_input, cur_simg_input)
|
||||
# self.bottleneck_t = bottleneck_model(cur_timg_input)
|
||||
else:
|
||||
self.bottleneck_t = self.bottleneck_t_raw
|
||||
|
||||
bottleneck_diff = self.bottleneck_t - self.bottleneck_a
|
||||
scale_factor = tf.sqrt(tf.reduce_sum(tf.square(self.bottleneck_t), axis=1))
|
||||
|
||||
cur_bottlesim = tf.sqrt(tf.reduce_sum(tf.square(bottleneck_diff), axis=1))
|
||||
cur_bottlesim = cur_bottlesim / scale_factor
|
||||
cur_bottlesim_sum = tf.reduce_sum(cur_bottlesim)
|
||||
|
||||
self.bottlesim += cur_bottlesim
|
||||
|
||||
# self.bottlesim_push += cur_bottlesim_push_sum
|
||||
self.bottlesim_sum += cur_bottlesim_sum
|
||||
|
||||
# sum up the losses
|
||||
if self.maximize:
|
||||
self.loss = self.const * tf.square(self.dist) - self.bottlesim
|
||||
else:
|
||||
self.loss = self.const * tf.square(self.dist) + self.bottlesim
|
||||
|
||||
self.loss_sum = tf.reduce_sum(tf.where(self.mask, self.loss, tf.zeros_like(self.loss)))
|
||||
|
||||
# Setup the Adadelta optimizer and keep track of variables
|
||||
# we're creating
|
||||
start_vars = set(x.name for x in tf.global_variables())
|
||||
self.learning_rate_holder = tf.placeholder(tf.float32, shape=[])
|
||||
# optimizer = tf.train.AdadeltaOptimizer(self.learning_rate_holder)
|
||||
optimizer = tf.train.AdamOptimizer(self.learning_rate_holder)
|
||||
|
||||
self.train = optimizer.minimize(self.loss_sum,
|
||||
var_list=[self.modifier])
|
||||
end_vars = tf.global_variables()
|
||||
new_vars = [x for x in end_vars if x.name not in start_vars]
|
||||
|
||||
# these are the variables to initialize when we run
|
||||
self.setup = []
|
||||
self.setup.append(self.modifier.assign(self.assign_modifier))
|
||||
if self.MIMIC_IMG:
|
||||
self.setup.append(self.timg_tanh.assign(self.assign_timg_tanh))
|
||||
else:
|
||||
self.setup.append(self.bottleneck_t_raw.assign(
|
||||
self.assign_bottleneck_t_raw))
|
||||
self.setup.append(self.simg_tanh.assign(self.assign_simg_tanh))
|
||||
self.setup.append(self.const.assign(self.assign_const))
|
||||
self.setup.append(self.mask.assign(self.assign_mask))
|
||||
self.setup.append(self.weights.assign(self.assign_weights))
|
||||
|
||||
self.init = tf.variables_initializer(var_list=[self.modifier] + new_vars)
|
||||
|
||||
print('Attacker loaded')
|
||||
|
||||
def preprocess_arctanh(self, imgs):
|
||||
|
||||
imgs = reverse_preprocess(imgs, self.intensity_range)
|
||||
imgs /= 255.0
|
||||
imgs -= 0.5
|
||||
imgs *= self.tanh_constant
|
||||
tanh_imgs = np.arctanh(imgs)
|
||||
|
||||
return tanh_imgs
|
||||
|
||||
def clipping(self, imgs):
|
||||
|
||||
imgs = reverse_preprocess(imgs, self.intensity_range)
|
||||
imgs = np.clip(imgs, 0, self.max_val)
|
||||
imgs = np.rint(imgs)
|
||||
|
||||
imgs = preprocess(imgs, self.intensity_range)
|
||||
|
||||
return imgs
|
||||
|
||||
def attack(self, source_imgs, target_imgs, weights=None):
|
||||
|
||||
if weights is None:
|
||||
weights = np.ones([source_imgs.shape[0]] +
|
||||
list(self.bottleneck_shape[1:]))
|
||||
|
||||
assert weights.shape[1:] == self.bottleneck_shape[1:]
|
||||
assert source_imgs.shape[1:] == self.input_shape[1:]
|
||||
assert source_imgs.shape[0] == weights.shape[0]
|
||||
if self.MIMIC_IMG:
|
||||
assert target_imgs.shape[1:] == self.input_shape[1:]
|
||||
assert source_imgs.shape[0] == target_imgs.shape[0]
|
||||
else:
|
||||
assert target_imgs.shape[1:] == self.bottleneck_shape[1:]
|
||||
assert source_imgs.shape[0] == target_imgs.shape[0]
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
adv_imgs = []
|
||||
print('%d batches in total'
|
||||
% int(np.ceil(len(source_imgs) / self.batch_size)))
|
||||
|
||||
for idx in range(0, len(source_imgs), self.batch_size):
|
||||
print('processing batch %d at %s' % (idx, datetime.datetime.now()))
|
||||
adv_img = self.attack_batch(source_imgs[idx:idx + self.batch_size],
|
||||
target_imgs[idx:idx + self.batch_size],
|
||||
weights[idx:idx + self.batch_size])
|
||||
adv_imgs.extend(adv_img)
|
||||
|
||||
elapsed_time = time.time() - start_time
|
||||
print('attack cost %f s' % (elapsed_time))
|
||||
|
||||
return np.array(adv_imgs)
|
||||
|
||||
def attack_batch(self, source_imgs, target_imgs, weights):
|
||||
|
||||
"""
|
||||
Run the attack on a batch of images and labels.
|
||||
"""
|
||||
|
||||
LR = self.learning_rate
|
||||
nb_imgs = source_imgs.shape[0]
|
||||
mask = [True] * nb_imgs + [False] * (self.batch_size - nb_imgs)
|
||||
mask = np.array(mask, dtype=np.bool)
|
||||
|
||||
source_imgs = np.array(source_imgs)
|
||||
target_imgs = np.array(target_imgs)
|
||||
|
||||
# convert to tanh-space
|
||||
simg_tanh = self.preprocess_arctanh(source_imgs)
|
||||
if self.MIMIC_IMG:
|
||||
timg_tanh = self.preprocess_arctanh(target_imgs)
|
||||
else:
|
||||
timg_tanh = target_imgs
|
||||
|
||||
CONST = np.ones(self.batch_size) * self.initial_const
|
||||
|
||||
self.sess.run(self.init)
|
||||
simg_tanh_batch = np.zeros(self.input_shape)
|
||||
if self.MIMIC_IMG:
|
||||
timg_tanh_batch = np.zeros(self.input_shape)
|
||||
else:
|
||||
timg_tanh_batch = np.zeros(self.bottleneck_shape)
|
||||
weights_batch = np.zeros(self.bottleneck_shape)
|
||||
simg_tanh_batch[:nb_imgs] = simg_tanh[:nb_imgs]
|
||||
timg_tanh_batch[:nb_imgs] = timg_tanh[:nb_imgs]
|
||||
weights_batch[:nb_imgs] = weights[:nb_imgs]
|
||||
modifier_batch = np.ones(self.input_shape) * 1e-6
|
||||
|
||||
# set the variables so that we don't have to send them over again
|
||||
if self.MIMIC_IMG:
|
||||
self.sess.run(self.setup,
|
||||
{self.assign_timg_tanh: timg_tanh_batch,
|
||||
self.assign_simg_tanh: simg_tanh_batch,
|
||||
self.assign_const: CONST,
|
||||
self.assign_mask: mask,
|
||||
self.assign_weights: weights_batch,
|
||||
self.assign_modifier: modifier_batch})
|
||||
else:
|
||||
# if directly mimicking a vector, use assign_bottleneck_t_raw
|
||||
# in setup
|
||||
self.sess.run(self.setup,
|
||||
{self.assign_bottleneck_t_raw: timg_tanh_batch,
|
||||
self.assign_simg_tanh: simg_tanh_batch,
|
||||
self.assign_const: CONST,
|
||||
self.assign_mask: mask,
|
||||
self.assign_weights: weights_batch,
|
||||
self.assign_modifier: modifier_batch})
|
||||
|
||||
best_bottlesim = [0] * nb_imgs if self.maximize else [np.inf] * nb_imgs
|
||||
best_adv = np.zeros_like(source_imgs)
|
||||
|
||||
if self.verbose == 1:
|
||||
loss_sum = float(self.sess.run(self.loss_sum))
|
||||
dist_sum = float(self.sess.run(self.dist_sum))
|
||||
thresh_over = (dist_sum / self.batch_size / self.l_threshold * 100)
|
||||
dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
|
||||
bottlesim_sum = self.sess.run(self.bottlesim_sum)
|
||||
print('START: Total loss: %.4E; perturb: %.6f (%.2f%% over, raw: %.6f); sim: %f'
|
||||
% (Decimal(loss_sum),
|
||||
dist_sum,
|
||||
thresh_over,
|
||||
dist_raw_sum,
|
||||
bottlesim_sum / nb_imgs))
|
||||
|
||||
try:
|
||||
total_distance = [0] * nb_imgs
|
||||
|
||||
if self.limit_dist:
|
||||
dist_raw_list, bottlesim_list, aimg_input_list = self.sess.run(
|
||||
[self.dist_raw,
|
||||
self.bottlesim,
|
||||
self.aimg_input])
|
||||
for e, (dist_raw, bottlesim, aimg_input) in enumerate(
|
||||
zip(dist_raw_list, bottlesim_list, aimg_input_list)):
|
||||
if e >= nb_imgs:
|
||||
break
|
||||
total_distance[e] = bottlesim
|
||||
|
||||
for iteration in range(self.MAX_ITERATIONS):
|
||||
|
||||
self.sess.run([self.train], feed_dict={self.learning_rate_holder: LR})
|
||||
|
||||
dist_raw_list, bottlesim_list, aimg_input_list = self.sess.run(
|
||||
[self.dist_raw,
|
||||
self.bottlesim,
|
||||
self.aimg_input])
|
||||
for e, (dist_raw, bottlesim, aimg_input) in enumerate(
|
||||
zip(dist_raw_list, bottlesim_list, aimg_input_list)):
|
||||
if e >= nb_imgs:
|
||||
break
|
||||
if (bottlesim < best_bottlesim[e] and bottlesim > total_distance[e] * 0.1 and (
|
||||
not self.maximize)) or (
|
||||
bottlesim > best_bottlesim[e] and self.maximize):
|
||||
best_bottlesim[e] = bottlesim
|
||||
best_adv[e] = aimg_input
|
||||
|
||||
if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0:
|
||||
LR = LR / 2
|
||||
print("Learning Rate: ", LR)
|
||||
|
||||
if iteration % (self.MAX_ITERATIONS // 10) == 0:
|
||||
if self.verbose == 1:
|
||||
loss_sum = float(self.sess.run(self.loss_sum))
|
||||
dist_sum = float(self.sess.run(self.dist_sum))
|
||||
thresh_over = (dist_sum /
|
||||
self.batch_size /
|
||||
self.l_threshold *
|
||||
100)
|
||||
dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
|
||||
bottlesim_sum = self.sess.run(self.bottlesim_sum)
|
||||
print('ITER %4d: Total loss: %.4E; perturb: %.6f (%.2f%% over, raw: %.6f); sim: %f'
|
||||
% (iteration,
|
||||
Decimal(loss_sum),
|
||||
dist_sum,
|
||||
thresh_over,
|
||||
dist_raw_sum,
|
||||
bottlesim_sum / nb_imgs))
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
|
||||
if self.verbose == 1:
|
||||
loss_sum = float(self.sess.run(self.loss_sum))
|
||||
dist_sum = float(self.sess.run(self.dist_sum))
|
||||
thresh_over = (dist_sum / self.batch_size / self.l_threshold * 100)
|
||||
dist_raw_sum = float(self.sess.run(self.dist_raw_sum))
|
||||
bottlesim_sum = float(self.sess.run(self.bottlesim_sum))
|
||||
print('END: Total loss: %.4E; perturb: %.6f (%.2f%% over, raw: %.6f); sim: %f'
|
||||
% (Decimal(loss_sum),
|
||||
dist_sum,
|
||||
thresh_over,
|
||||
dist_raw_sum,
|
||||
bottlesim_sum / nb_imgs))
|
||||
|
||||
best_adv = self.clipping(best_adv[:nb_imgs])
|
||||
|
||||
return best_adv
|
||||
|
|
@ -0,0 +1,95 @@
|
|||
import argparse
|
||||
import os
|
||||
import pickle
|
||||
import random
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
from differentiator import FawkesMaskGeneration
|
||||
from tensorflow import set_random_seed
|
||||
from utils import load_extractor, CloakData, init_gpu
|
||||
|
||||
random.seed(12243)
|
||||
np.random.seed(122412)
|
||||
set_random_seed(12242)
|
||||
|
||||
NUM_IMG_PROTECTED = 32 # Number of images used to optimize the target class
|
||||
BATCH_SIZE = 32
|
||||
|
||||
MAX_ITER = 1000
|
||||
|
||||
|
||||
def diff_protected_data(sess, feature_extractors_ls, image_X, number_protect, target_X=None, th=0.01):
|
||||
image_X = image_X[:number_protect]
|
||||
differentiator = FawkesMaskGeneration(sess, feature_extractors_ls,
|
||||
batch_size=BATCH_SIZE,
|
||||
mimic_img=True,
|
||||
intensity_range='imagenet',
|
||||
initial_const=args.sd,
|
||||
learning_rate=args.lr,
|
||||
max_iterations=MAX_ITER,
|
||||
l_threshold=th,
|
||||
verbose=1, maximize=False, keep_final=False, image_shape=image_X.shape[1:])
|
||||
|
||||
if len(target_X) < len(image_X):
|
||||
target_X = np.concatenate([target_X, target_X, target_X])
|
||||
target_X = target_X[:len(image_X)]
|
||||
cloaked_image_X = differentiator.attack(image_X, target_X)
|
||||
return cloaked_image_X
|
||||
|
||||
|
||||
def perform_defense():
|
||||
RES = {}
|
||||
sess = init_gpu(args.gpu)
|
||||
|
||||
FEATURE_EXTRACTORS = [args.feature_extractor]
|
||||
RES_DIR = '../results/'
|
||||
|
||||
RES['num_img_protected'] = NUM_IMG_PROTECTED
|
||||
RES['extractors'] = FEATURE_EXTRACTORS
|
||||
num_protect = NUM_IMG_PROTECTED
|
||||
|
||||
print("Loading {} for optimization".format(args.feature_extractor))
|
||||
feature_extractors_ls = [load_extractor(name) for name in FEATURE_EXTRACTORS]
|
||||
protect_class = args.protect_class
|
||||
|
||||
cloak_data = CloakData(args.dataset, protect_class=protect_class)
|
||||
RES_FILE_NAME = "{}_{}_protect{}".format(args.dataset, args.feature_extractor, cloak_data.protect_class)
|
||||
RES_FILE_NAME = os.path.join(RES_DIR, RES_FILE_NAME)
|
||||
print("Protect Class: ", cloak_data.protect_class)
|
||||
|
||||
cloak_data.target_path, cloak_data.target_data = cloak_data.select_target_label(feature_extractors_ls,
|
||||
FEATURE_EXTRACTORS)
|
||||
|
||||
os.makedirs(RES_DIR, exist_ok=True)
|
||||
os.makedirs(RES_FILE_NAME, exist_ok=True)
|
||||
|
||||
cloak_image_X = diff_protected_data(sess, feature_extractors_ls, cloak_data.protect_train_X,
|
||||
number_protect=num_protect,
|
||||
target_X=cloak_data.target_data, th=args.th)
|
||||
|
||||
cloak_data.cloaked_protect_train_X = cloak_image_X
|
||||
RES['cloak_data'] = cloak_data
|
||||
pickle.dump(RES, open(os.path.join(RES_FILE_NAME, 'cloak_data.p'), "wb"))
|
||||
|
||||
|
||||
def parse_arguments(argv):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--gpu', type=str,
|
||||
help='GPU id', default='0')
|
||||
parser.add_argument('--dataset', type=str,
|
||||
help='name of dataset', default='scrub')
|
||||
parser.add_argument('--feature-extractor', type=str,
|
||||
help="name of the feature extractor used for optimization",
|
||||
default="webface_dense_robust_extract")
|
||||
parser.add_argument('--th', type=float, default=0.007)
|
||||
parser.add_argument('--sd', type=int, default=1e5)
|
||||
parser.add_argument('--protect_class', type=str, default=None)
|
||||
parser.add_argument('--lr', type=float, default=0.1)
|
||||
|
||||
return parser.parse_args(argv)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_arguments(sys.argv[1:])
|
||||
perform_defense()
|
||||
|
|
@ -0,0 +1,372 @@
|
|||
import json
|
||||
import os
|
||||
import pickle
|
||||
import random
|
||||
|
||||
import keras
|
||||
import keras.backend as K
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from keras.applications.vgg16 import preprocess_input
|
||||
from keras.layers import Dense, Activation
|
||||
from keras.models import Model
|
||||
from keras.preprocessing import image
|
||||
from keras.utils import to_categorical
|
||||
from sklearn.metrics import pairwise_distances
|
||||
|
||||
|
||||
def clip_img(X, preprocessing='raw'):
|
||||
X = reverse_preprocess(X, preprocessing)
|
||||
X = np.clip(X, 0.0, 255.0)
|
||||
X = preprocess(X, preprocessing)
|
||||
return X
|
||||
|
||||
|
||||
def dump_dictionary_as_json(dict, outfile):
|
||||
j = json.dumps(dict)
|
||||
with open(outfile, "wb") as f:
|
||||
f.write(j.encode())
|
||||
|
||||
|
||||
def fix_gpu_memory(mem_fraction=1):
|
||||
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
|
||||
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_fraction)
|
||||
tf_config = tf.ConfigProto(gpu_options=gpu_options)
|
||||
tf_config.gpu_options.allow_growth = True
|
||||
tf_config.log_device_placement = False
|
||||
init_op = tf.global_variables_initializer()
|
||||
sess = tf.Session(config=tf_config)
|
||||
sess.run(init_op)
|
||||
K.set_session(sess)
|
||||
return sess
|
||||
|
||||
|
||||
def load_victim_model(number_classes, teacher_model=None, end2end=False):
|
||||
for l in teacher_model.layers:
|
||||
l.trainable = end2end
|
||||
x = teacher_model.layers[-1].output
|
||||
|
||||
x = Dense(number_classes)(x)
|
||||
x = Activation('softmax', name="act")(x)
|
||||
model = Model(teacher_model.input, x)
|
||||
opt = keras.optimizers.Adadelta()
|
||||
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
|
||||
return model
|
||||
|
||||
|
||||
def init_gpu(gpu_index, force=False):
|
||||
if isinstance(gpu_index, list):
|
||||
gpu_num = ','.join([str(i) for i in gpu_index])
|
||||
else:
|
||||
gpu_num = str(gpu_index)
|
||||
if "CUDA_VISIBLE_DEVICES" in os.environ and os.environ["CUDA_VISIBLE_DEVICES"] and not force:
|
||||
print('GPU already initiated')
|
||||
return
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_num
|
||||
sess = fix_gpu_memory()
|
||||
return sess
|
||||
|
||||
|
||||
def preprocess(X, method):
|
||||
assert method in {'raw', 'imagenet', 'inception', 'mnist'}
|
||||
|
||||
if method is 'raw':
|
||||
pass
|
||||
elif method is 'imagenet':
|
||||
X = imagenet_preprocessing(X)
|
||||
else:
|
||||
raise Exception('unknown method %s' % method)
|
||||
|
||||
return X
|
||||
|
||||
|
||||
def reverse_preprocess(X, method):
|
||||
assert method in {'raw', 'imagenet', 'inception', 'mnist'}
|
||||
|
||||
if method is 'raw':
|
||||
pass
|
||||
elif method is 'imagenet':
|
||||
X = imagenet_reverse_preprocessing(X)
|
||||
else:
|
||||
raise Exception('unknown method %s' % method)
|
||||
|
||||
return X
|
||||
|
||||
|
||||
def imagenet_preprocessing(x, data_format=None):
|
||||
if data_format is None:
|
||||
data_format = K.image_data_format()
|
||||
assert data_format in ('channels_last', 'channels_first')
|
||||
|
||||
x = np.array(x)
|
||||
if data_format == 'channels_first':
|
||||
# 'RGB'->'BGR'
|
||||
if x.ndim == 3:
|
||||
x = x[::-1, ...]
|
||||
else:
|
||||
x = x[:, ::-1, ...]
|
||||
else:
|
||||
# 'RGB'->'BGR'
|
||||
x = x[..., ::-1]
|
||||
|
||||
mean = [103.939, 116.779, 123.68]
|
||||
std = None
|
||||
|
||||
# Zero-center by mean pixel
|
||||
if data_format == 'channels_first':
|
||||
if x.ndim == 3:
|
||||
x[0, :, :] -= mean[0]
|
||||
x[1, :, :] -= mean[1]
|
||||
x[2, :, :] -= mean[2]
|
||||
if std is not None:
|
||||
x[0, :, :] /= std[0]
|
||||
x[1, :, :] /= std[1]
|
||||
x[2, :, :] /= std[2]
|
||||
else:
|
||||
x[:, 0, :, :] -= mean[0]
|
||||
x[:, 1, :, :] -= mean[1]
|
||||
x[:, 2, :, :] -= mean[2]
|
||||
if std is not None:
|
||||
x[:, 0, :, :] /= std[0]
|
||||
x[:, 1, :, :] /= std[1]
|
||||
x[:, 2, :, :] /= std[2]
|
||||
else:
|
||||
x[..., 0] -= mean[0]
|
||||
x[..., 1] -= mean[1]
|
||||
x[..., 2] -= mean[2]
|
||||
if std is not None:
|
||||
x[..., 0] /= std[0]
|
||||
x[..., 1] /= std[1]
|
||||
x[..., 2] /= std[2]
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def imagenet_reverse_preprocessing(x, data_format=None):
|
||||
import keras.backend as K
|
||||
x = np.array(x)
|
||||
if data_format is None:
|
||||
data_format = K.image_data_format()
|
||||
assert data_format in ('channels_last', 'channels_first')
|
||||
|
||||
if data_format == 'channels_first':
|
||||
if x.ndim == 3:
|
||||
# Zero-center by mean pixel
|
||||
x[0, :, :] += 103.939
|
||||
x[1, :, :] += 116.779
|
||||
x[2, :, :] += 123.68
|
||||
# 'BGR'->'RGB'
|
||||
x = x[::-1, :, :]
|
||||
else:
|
||||
x[:, 0, :, :] += 103.939
|
||||
x[:, 1, :, :] += 116.779
|
||||
x[:, 2, :, :] += 123.68
|
||||
x = x[:, ::-1, :, :]
|
||||
else:
|
||||
# Zero-center by mean pixel
|
||||
x[..., 0] += 103.939
|
||||
x[..., 1] += 116.779
|
||||
x[..., 2] += 123.68
|
||||
# 'BGR'->'RGB'
|
||||
x = x[..., ::-1]
|
||||
return x
|
||||
|
||||
|
||||
def build_bottleneck_model(model, cut_off):
|
||||
bottleneck_model = Model(model.input, model.get_layer(cut_off).output)
|
||||
bottleneck_model.compile(loss='categorical_crossentropy',
|
||||
optimizer='adam',
|
||||
metrics=['accuracy'])
|
||||
return bottleneck_model
|
||||
|
||||
|
||||
def load_extractor(name):
|
||||
model = keras.models.load_model("../feature_extractors/{}.h5".format(name))
|
||||
if hasattr(model.layers[-1], "activation") and model.layers[-1].activation == "softmax":
|
||||
raise Exception(
|
||||
"Given extractor's last layer is softmax, need to remove the top layers to make it into a feature extractor")
|
||||
# if "extract" in name.split("/")[-1]:
|
||||
# pass
|
||||
# else:
|
||||
# print("Convert a model to a feature extractor")
|
||||
# model = build_bottleneck_model(model, model.layers[layer_idx].name)
|
||||
# model.save(name + "extract")
|
||||
# model = keras.models.load_model(name + "extract")
|
||||
return model
|
||||
|
||||
|
||||
def get_dataset_path(dataset):
|
||||
if not os.path.exists("config.json"):
|
||||
raise Exception("Please config the datasets before running protection code. See more in README and config.py.")
|
||||
|
||||
config = json.load(open("config.json", 'r'))
|
||||
if dataset not in config:
|
||||
raise Exception(
|
||||
"Dataset {} does not exist, please download to data/ and add the path to this function... Abort".format(
|
||||
dataset))
|
||||
return config[dataset]['train_dir'], config[dataset]['test_dir'], config[dataset]['num_classes'], config[dataset][
|
||||
'num_images']
|
||||
|
||||
|
||||
def normalize(x):
|
||||
return x / np.linalg.norm(x, axis=1, keepdims=True)
|
||||
|
||||
|
||||
class CloakData(object):
|
||||
def __init__(self, dataset, img_shape=(224, 224), protect_class=None):
|
||||
self.dataset = dataset
|
||||
self.img_shape = img_shape
|
||||
|
||||
self.train_data_dir, self.test_data_dir, self.number_classes, self.number_samples = get_dataset_path(dataset)
|
||||
self.all_labels = sorted(list(os.listdir(self.train_data_dir)))
|
||||
if protect_class:
|
||||
self.protect_class = protect_class
|
||||
else:
|
||||
self.protect_class = random.choice(self.all_labels)
|
||||
|
||||
self.sybil_class = random.choice([l for l in self.all_labels if l != self.protect_class])
|
||||
self.protect_train_X, self.protect_test_X = self.load_label_data(self.protect_class)
|
||||
self.sybil_train_X, self.sybil_test_X = self.load_label_data(self.sybil_class)
|
||||
|
||||
self.cloaked_protect_train_X = None
|
||||
self.cloaked_sybil_train_X = None
|
||||
|
||||
self.label2path_train, self.label2path_test, self.path2idx = self.build_data_mapping()
|
||||
self.all_training_path = self.get_all_data_path(self.label2path_train)
|
||||
self.all_test_path = self.get_all_data_path(self.label2path_test)
|
||||
self.protect_class_path = self.get_class_image_files(os.path.join(self.train_data_dir, self.protect_class))
|
||||
self.sybil_class_path = self.get_class_image_files(os.path.join(self.train_data_dir, self.sybil_class))
|
||||
|
||||
print("Find {} protect images".format(len(self.protect_class_path)))
|
||||
|
||||
def get_class_image_files(self, path):
|
||||
return [os.path.join(path, f) for f in os.listdir(path)]
|
||||
|
||||
def extractor_ls_predict(self, feature_extractors_ls, X):
|
||||
feature_ls = []
|
||||
for extractor in feature_extractors_ls:
|
||||
cur_features = extractor.predict(X)
|
||||
feature_ls.append(cur_features)
|
||||
concated_feature_ls = np.concatenate(feature_ls, axis=1)
|
||||
concated_feature_ls = normalize(concated_feature_ls)
|
||||
return concated_feature_ls
|
||||
|
||||
def load_embeddings(self, feature_extractors_names):
|
||||
dictionaries = []
|
||||
for extractor_name in feature_extractors_names:
|
||||
path2emb = pickle.load(open("../feature_extractors/embeddings/{}_emb_norm.p".format(extractor_name), "rb"))
|
||||
dictionaries.append(path2emb)
|
||||
|
||||
merge_dict = {}
|
||||
for k in dictionaries[0].keys():
|
||||
cur_emb = [dic[k] for dic in dictionaries]
|
||||
merge_dict[k] = np.concatenate(cur_emb)
|
||||
return merge_dict
|
||||
|
||||
def select_target_label(self, feature_extractors_ls, feature_extractors_names, metric='l2'):
|
||||
original_feature_x = self.extractor_ls_predict(feature_extractors_ls, self.protect_train_X)
|
||||
|
||||
path2emb = self.load_embeddings(feature_extractors_names)
|
||||
items = list(path2emb.items())
|
||||
paths = [p[0] for p in items]
|
||||
embs = [p[1] for p in items]
|
||||
embs = np.array(embs)
|
||||
|
||||
pair_dist = pairwise_distances(original_feature_x, embs, metric)
|
||||
max_sum = np.min(pair_dist, axis=0)
|
||||
sorted_idx = np.argsort(max_sum)[::-1]
|
||||
|
||||
highest_num = 0
|
||||
paired_target_X = None
|
||||
final_target_class_path = None
|
||||
for idx in sorted_idx[:5]:
|
||||
target_class_path = paths[idx]
|
||||
cur_target_X = self.load_dir(target_class_path)
|
||||
cur_target_X = np.concatenate([cur_target_X, cur_target_X, cur_target_X])
|
||||
cur_tot_sum, cur_paired_target_X = self.calculate_dist_score(self.protect_train_X, cur_target_X,
|
||||
feature_extractors_ls,
|
||||
metric=metric)
|
||||
if cur_tot_sum > highest_num:
|
||||
highest_num = cur_tot_sum
|
||||
paired_target_X = cur_paired_target_X
|
||||
final_target_class_path = target_class_path
|
||||
|
||||
np.random.shuffle(paired_target_X)
|
||||
return final_target_class_path, paired_target_X
|
||||
|
||||
def calculate_dist_score(self, a, b, feature_extractors_ls, metric='l2'):
|
||||
features1 = self.extractor_ls_predict(feature_extractors_ls, a)
|
||||
features2 = self.extractor_ls_predict(feature_extractors_ls, b)
|
||||
|
||||
pair_cos = pairwise_distances(features1, features2, metric)
|
||||
max_sum = np.min(pair_cos, axis=0)
|
||||
max_sum_arg = np.argsort(max_sum)[::-1]
|
||||
max_sum_arg = max_sum_arg[:len(a)]
|
||||
max_sum = [max_sum[i] for i in max_sum_arg]
|
||||
paired_target_X = [b[j] for j in max_sum_arg]
|
||||
paired_target_X = np.array(paired_target_X)
|
||||
return np.min(max_sum), paired_target_X
|
||||
|
||||
def get_all_data_path(self, label2path):
|
||||
all_paths = []
|
||||
for k, v in label2path.items():
|
||||
cur_all_paths = [os.path.join(k, cur_p) for cur_p in v]
|
||||
all_paths.extend(cur_all_paths)
|
||||
return all_paths
|
||||
|
||||
def load_label_data(self, label):
|
||||
train_label_path = os.path.join(self.train_data_dir, label)
|
||||
test_label_path = os.path.join(self.test_data_dir, label)
|
||||
train_X = self.load_dir(train_label_path)
|
||||
test_X = self.load_dir(test_label_path)
|
||||
return train_X, test_X
|
||||
|
||||
def load_dir(self, path):
|
||||
assert os.path.exists(path)
|
||||
x_ls = []
|
||||
for file in os.listdir(path):
|
||||
cur_path = os.path.join(path, file)
|
||||
im = image.load_img(cur_path, target_size=self.img_shape)
|
||||
im = image.img_to_array(im)
|
||||
x_ls.append(im)
|
||||
raw_x = np.array(x_ls)
|
||||
return preprocess_input(raw_x)
|
||||
|
||||
def build_data_mapping(self):
|
||||
label2path_train = {}
|
||||
label2path_test = {}
|
||||
idx = 0
|
||||
path2idx = {}
|
||||
for label_name in self.all_labels:
|
||||
full_path_train = os.path.join(self.train_data_dir, label_name)
|
||||
full_path_test = os.path.join(self.test_data_dir, label_name)
|
||||
label2path_train[full_path_train] = list(os.listdir(full_path_train))
|
||||
label2path_test[full_path_test] = list(os.listdir(full_path_test))
|
||||
for img_file in os.listdir(full_path_train):
|
||||
path2idx[os.path.join(full_path_train, img_file)] = idx
|
||||
for img_file in os.listdir(full_path_test):
|
||||
path2idx[os.path.join(full_path_test, img_file)] = idx
|
||||
idx += 1
|
||||
return label2path_train, label2path_test, path2idx
|
||||
|
||||
def generate_data_post_cloak(self, sybil=False):
|
||||
assert self.cloaked_protect_train_X is not None
|
||||
while True:
|
||||
batch_X = []
|
||||
batch_Y = []
|
||||
cur_batch_path = random.sample(self.all_training_path, 32)
|
||||
for p in cur_batch_path:
|
||||
cur_y = self.path2idx[p]
|
||||
if p in self.protect_class_path:
|
||||
cur_x = random.choice(self.cloaked_protect_train_X)
|
||||
elif sybil and (p in self.sybil_class):
|
||||
cur_x = random.choice(self.cloaked_sybil_train_X)
|
||||
else:
|
||||
im = image.load_img(p, target_size=self.img_shape)
|
||||
im = image.img_to_array(im)
|
||||
cur_x = preprocess_input(im)
|
||||
batch_X.append(cur_x)
|
||||
batch_Y.append(cur_y)
|
||||
batch_X = np.array(batch_X)
|
||||
batch_Y = to_categorical(np.array(batch_Y), num_classes=self.number_classes)
|
||||
yield batch_X, batch_Y
|
||||
Ładowanie…
Reference in New Issue