diff --git a/fawkes/differentiator.py b/fawkes/differentiator.py index 38dfada..4031f35 100644 --- a/fawkes/differentiator.py +++ b/fawkes/differentiator.py @@ -136,18 +136,21 @@ class FawkesMaskGeneration: 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 + # dist = tf.square(aimg_raw_split - simg_raw_split) 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 = self.dist_raw 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))) + # self.dist_sum = 1e-5 * tf.reduce_sum(self.dist) + # self.dist_raw_sum = self.dist_sum def resize_tensor(input_tensor, model_input_shape): if input_tensor.shape[1:] == model_input_shape or model_input_shape[1] is None: @@ -158,12 +161,6 @@ class FawkesMaskGeneration: 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 @@ -201,7 +198,14 @@ class FawkesMaskGeneration: 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))) + self.loss_sum = tf.reduce_sum(tf.where(self.mask, + self.loss, + tf.zeros_like(self.loss))) + + # self.loss_sum = self.dist_sum + tf.reduce_sum(self.bottlesim) + # import pdb + # pdb.set_trace() + # 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 @@ -321,25 +325,13 @@ class FawkesMaskGeneration: 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}) + 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}) best_bottlesim = [0] * nb_imgs if self.maximize else [np.inf] * nb_imgs best_adv = np.zeros_like(source_imgs) @@ -390,7 +382,7 @@ class FawkesMaskGeneration: best_adv[e] = aimg_input if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0: - LR = LR / 2 + # LR = LR / 2 print("Learning Rate: ", LR) if iteration % (self.MAX_ITERATIONS // 10) == 0: diff --git a/fawkes/protection.py b/fawkes/protection.py index f114fb1..228b93a 100644 --- a/fawkes/protection.py +++ b/fawkes/protection.py @@ -1,28 +1,29 @@ import argparse +import glob import os -import pickle import random import sys import numpy as np from differentiator import FawkesMaskGeneration +from keras.applications.vgg16 import preprocess_input +from keras.preprocessing import image +from skimage.transform import resize from tensorflow import set_random_seed -from utils import load_extractor, CloakData, init_gpu +from utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked 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, +def generate_cloak_images(sess, feature_extractors, image_X, target_X=None, th=0.01): + batch_size = BATCH_SIZE if len(image_X) > BATCH_SIZE else len(image_X) + differentiator = FawkesMaskGeneration(sess, feature_extractors, + batch_size=batch_size, mimic_img=True, intensity_range='imagenet', initial_const=args.sd, @@ -31,65 +32,81 @@ def diff_protected_data(sess, feature_extractors_ls, image_X, number_protect, ta 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 = {} +def extract_faces(img): + # foo + return preprocess_input(resize(img, (224, 224))) + + +def fawkes(): + assert os.path.exists(args.directory) + assert os.path.isdir(args.directory) + 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) + feature_extractors_ls = [load_extractor(args.feature_extractor)] - cloak_data.target_path, cloak_data.target_data = cloak_data.select_target_label(feature_extractors_ls, - FEATURE_EXTRACTORS) + image_paths = glob.glob(os.path.join(args.directory, "*")) + image_paths = [path for path in image_paths if "_cloaked" not in path.split("/")[-1]] - os.makedirs(RES_DIR, exist_ok=True) - os.makedirs(RES_FILE_NAME, exist_ok=True) + orginal_images = [extract_faces(image.img_to_array(image.load_img(cur_path))) for cur_path in + image_paths] - 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) + orginal_images = np.array(orginal_images) - 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")) + if args.seperate_target: + target_images = [] + for org_img in orginal_images: + org_img = org_img.reshape([1] + list(org_img.shape)) + tar_img = select_target_label(org_img, feature_extractors_ls, [args.feature_extractor]) + target_images.append(tar_img) + target_images = np.concatenate(target_images) + # import pdb + # pdb.set_trace() + else: + target_images = select_target_label(orginal_images, feature_extractors_ls, [args.feature_extractor]) + + # file_name = args.directory.split("/")[-1] + # os.makedirs(args.result_directory, exist_ok=True) + # os.makedirs(os.path.join(args.result_directory, file_name), exist_ok=True) + + protected_images = generate_cloak_images(sess, feature_extractors_ls, orginal_images, + target_X=target_images, th=args.th) + + for p_img, path in zip(protected_images, image_paths): + p_img = reverse_process_cloaked(p_img) + # img_type = path.split(".")[-1] + file_name = "{}_cloaked.jpeg".format(".".join(path.split(".")[:-1])) + dump_image(p_img, file_name, format="JPEG") 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('--directory', type=str, + help='directory that contain images for cloaking', default='imgs/') + 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('--th', type=float, default=0.005) + parser.add_argument('--sd', type=int, default=1e10) parser.add_argument('--protect_class', type=str, default=None) parser.add_argument('--lr', type=float, default=0.1) + parser.add_argument('--result_directory', type=str, default="../results") + parser.add_argument('--seperate_target', action='store_true') + return parser.parse_args(argv) if __name__ == '__main__': args = parse_arguments(sys.argv[1:]) - perform_defense() + fawkes() diff --git a/fawkes/utils.py b/fawkes/utils.py index b2a9fe3..f153913 100644 --- a/fawkes/utils.py +++ b/fawkes/utils.py @@ -141,7 +141,6 @@ def imagenet_preprocessing(x, data_format=None): return x - def imagenet_reverse_preprocessing(x, data_format=None): import keras.backend as K x = np.array(x) @@ -172,6 +171,11 @@ def imagenet_reverse_preprocessing(x, data_format=None): return x +def reverse_process_cloaked(x, preprocess='imagenet'): + x = clip_img(x, preprocess) + return reverse_preprocess(x, preprocess) + + def build_bottleneck_model(model, cut_off): bottleneck_model = Model(model.input, model.get_layer(cut_off).output) bottleneck_model.compile(loss='categorical_crossentropy', @@ -212,32 +216,116 @@ def normalize(x): return x / np.linalg.norm(x, axis=1, keepdims=True) +def dump_image(x, filename, format="png", scale=False): + img = image.array_to_img(x, scale=scale) + img.save(filename, format) + return + + +def load_dir(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=(224, 224)) + im = image.img_to_array(im) + x_ls.append(im) + raw_x = np.array(x_ls) + return preprocess_input(raw_x) + + +def load_embeddings(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 extractor_ls_predict(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 calculate_dist_score(a, b, feature_extractors_ls, metric='l2'): + features1 = extractor_ls_predict(feature_extractors_ls, a) + features2 = 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 select_target_label(imgs, feature_extractors_ls, feature_extractors_names, metric='l2'): + original_feature_x = extractor_ls_predict(feature_extractors_ls, imgs) + + path2emb = 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[:1]: + target_class_path = paths[idx] + cur_target_X = 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 = calculate_dist_score(imgs, 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) + paired_target_X = list(paired_target_X) + while len(paired_target_X) < len(imgs): + paired_target_X += paired_target_X + + paired_target_X = paired_target_X[:len(imgs)] + return np.array(paired_target_X) + + + class CloakData(object): - def __init__(self, dataset, img_shape=(224, 224), protect_class=None): - self.dataset = dataset + def __init__(self, protect_directory=None, img_shape=(224, 224)): + 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.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))) + self.protect_directory = protect_directory - 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.protect_X = self.load_label_data(self.protect_directory) 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)] diff --git a/README.md b/fawkes_dev/README.md similarity index 90% rename from README.md rename to fawkes_dev/README.md index 88757d4..42f90b4 100644 --- a/README.md +++ b/fawkes_dev/README.md @@ -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. \ No newline at end of file +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} +} +``` \ No newline at end of file diff --git a/fawkes_dev/__init__.py b/fawkes_dev/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/fawkes/config.py b/fawkes_dev/config.py similarity index 100% rename from fawkes/config.py rename to fawkes_dev/config.py diff --git a/fawkes_dev/differentiator.py b/fawkes_dev/differentiator.py new file mode 100644 index 0000000..38dfada --- /dev/null +++ b/fawkes_dev/differentiator.py @@ -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 diff --git a/fawkes/eval_cloak.py b/fawkes_dev/eval_cloak.py similarity index 100% rename from fawkes/eval_cloak.py rename to fawkes_dev/eval_cloak.py diff --git a/fawkes/prepare_feature_extractor.py b/fawkes_dev/prepare_feature_extractor.py similarity index 100% rename from fawkes/prepare_feature_extractor.py rename to fawkes_dev/prepare_feature_extractor.py diff --git a/fawkes_dev/protection.py b/fawkes_dev/protection.py new file mode 100644 index 0000000..f114fb1 --- /dev/null +++ b/fawkes_dev/protection.py @@ -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() diff --git a/fawkes_dev/utils.py b/fawkes_dev/utils.py new file mode 100644 index 0000000..b2a9fe3 --- /dev/null +++ b/fawkes_dev/utils.py @@ -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