ResearchPapers/fawkes
ResearchPapers/fawkes
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standarize feature extractors

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Shawn-Shan 2020-06-01 07:50:02 -07:00
parent de558e841e
commit 83bb798373
6 changed files with 141 additions and 60 deletions
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42
fawkes/config.py Normal file
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@ -0,0 +1,42 @@
import glob
import json
import os
DATASETS = {
"pubfig": "../data/pubfig/",
"scrub": "../data/scrub/",
"vggface1": "/mnt/data/sixiongshan/data/vggface/",
# "vggface2": "/mnt/data/sixiongshan/data/vggface2/",
"webface": "/mnt/data/sixiongshan/data/webface/",
# "youtubeface": "/mnt/data/sixiongshan/data/youtubeface/keras_flow_data/",
}
def main():
config = {}
for dataset in DATASETS.keys():
path = DATASETS[dataset]
if not os.path.exists(path):
print("Dataset path for {} does not exist, skipped".format(dataset))
continue
train_dir = os.path.join(path, "train")
test_dir = os.path.join(path, "test")
if not os.path.exists(train_dir):
print("Training dataset path for {} does not exist, skipped".format(dataset))
continue
num_classes = len(os.listdir(train_dir))
num_images = len(glob.glob(os.path.join(train_dir, "*/*")))
if num_images == 0 or num_classes == 0 or num_images == num_classes:
raise Exception("Dataset {} is not setup as detailed in README.".format(dataset))
config[dataset] = {"train_dir": train_dir, "test_dir": test_dir, "num_classes": num_classes,
"num_images": num_images}
print("Successfully config {}".format(dataset))
j = json.dumps(config)
with open("config.json", "wb") as f:
f.write(j.encode())
if __name__ == '__main__':
main()

1
fawkes/differentiator.py
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@ -7,6 +7,7 @@
import datetime
import time
from decimal import Decimal
import numpy as np
import tensorflow as tf
from utils import preprocess, reverse_preprocess

12
fawkes/eval_cloak.py
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@ -102,9 +102,6 @@ def main():
else:
raise ValueError
CLOAK_DIR = CLOAK_DIR + "_th{}_sd{}".format(args.th, int(args.sd))
print(CLOAK_DIR)
CLOAK_DIR = os.path.join("../results", CLOAK_DIR)
RES = pickle.load(open(os.path.join(CLOAK_DIR, "cloak_data.p"), 'rb'))
@ -127,7 +124,7 @@ def main():
try:
model.fit_generator(train_generator, steps_per_epoch=cloak_data.number_samples // 32,
validation_data=(original_X, original_Y), epochs=args.n_epochs, verbose=1,
validation_data=(original_X, original_Y), epochs=args.n_epochs, verbose=2,
use_multiprocessing=False, workers=1)
except KeyboardInterrupt:
pass
@ -144,7 +141,8 @@ def main():
print("Accuracy on other classes {:.4f}".format(other_acc))
EVAL_RES['other_acc'] = other_acc
dump_dictionary_as_json(EVAL_RES,
os.path.join(CLOAK_DIR, "eval_seed{}_th{}.json".format(args.seed_idx, args.th)))
os.path.join(CLOAK_DIR,
"eval_seed{}_th{}_sd{}.json".format(args.seed_idx, args.th, args.sd)))
def parse_arguments(argv):
@ -158,7 +156,7 @@ def parse_arguments(argv):
help='name of dataset', default='scrub')
parser.add_argument('--cloak_data', type=str,
help='name of the cloak result directory',
default='scrub_webface_dense_robust_protectPatrick_Dempsey')
default='scrub_webface_dense_robust_protectKristen_Alderson')
parser.add_argument('--sd', type=int, default=1e6)
parser.add_argument('--th', type=float, default=0.01)
@ -167,7 +165,7 @@ def parse_arguments(argv):
help='student model', default='../feature_extractors/vggface2_inception_extract.h5')
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--validation_split', type=float, default=0.1)
parser.add_argument('--n_epochs', type=int, default=3)
parser.add_argument('--n_epochs', type=int, default=5)
return parser.parse_args(argv)

64
fawkes/prepare_feature_extractor.py Normal file
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@ -0,0 +1,64 @@
import argparse
import os
import pickle
import random
import sys
import numpy as np
from keras.applications.vgg16 import preprocess_input
from keras.preprocessing import image
from utils import load_extractor, get_dataset_path
def load_sample_dir(path, sample=10):
x_ls = []
image_paths = list(os.listdir(path))
random.shuffle(image_paths)
for i, file in enumerate(image_paths):
if i > sample:
break
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 normalize(x):
return x / np.linalg.norm(x)
def main():
extractor = load_extractor(args.feature_extractor)
path2emb = {}
for target_dataset in args.candidate_datasets:
target_dataset_path, _, _, _ = get_dataset_path(target_dataset)
for target_class in os.listdir(target_dataset_path):
target_class_path = os.path.join(target_dataset_path, target_class)
target_X = load_sample_dir(target_class_path)
cur_feature = extractor.predict(target_X)
cur_feature = np.mean(cur_feature, axis=0)
path2emb[target_class_path] = cur_feature
for k, v in path2emb.items():
path2emb[k] = normalize(v)
pickle.dump(path2emb, open("../feature_extractors/embeddings/{}_emb_norm.p".format(args.feature_extractor), "wb"))
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=str,
help='GPU id', default='0')
parser.add_argument('--candidate-datasets', nargs='+',
help='path candidate datasets')
parser.add_argument('--feature-extractor', type=str,
help="name of the feature extractor used for optimization",
default="webface_dense_robust_extract")
return parser.parse_args(argv)
if __name__ == '__main__':
args = parse_arguments(sys.argv[1:])
main()

22
fawkes/protection.py
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@ -9,13 +9,12 @@ 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 = 10 # Number of images used to optimize the target class
BATCH_SIZE = 10
NUM_IMG_PROTECTED = 32 # Number of images used to optimize the target class
BATCH_SIZE = 32
IMG_SHAPE = [224, 224, 3]
@ -53,16 +52,13 @@ def perform_defense():
num_protect = NUM_IMG_PROTECTED
print("Loading {} for optimization".format(args.feature_extractor))
feature_extractors_ls = [load_extractor(name, layer_idx=args.layer_idx) for name in FEATURE_EXTRACTORS]
feature_extractors_ls = [load_extractor(name) for name in FEATURE_EXTRACTORS]
protect_class = args.protect_class
cloak_data = CloakData(args.dataset, target_selection_tries=1, protect_class=protect_class)
model_name = args.feature_extractor.split("/")[-1].split('.')[0].replace("_extract", "")
RES_FILE_NAME = "{}_{}_protect{}_th{}_sd{}".format(args.dataset, model_name, cloak_data.protect_class, args.th,
args.sd)
RES_FILE_NAME = "{}_{}_protect{}".format(args.dataset, model_name, cloak_data.protect_class)
RES_FILE_NAME = os.path.join(RES_DIR, RES_FILE_NAME)
if os.path.exists(RES_FILE_NAME):
exit(1)
print("Protect Class: ", cloak_data.protect_class)
cloak_data.target_path, cloak_data.target_data = cloak_data.select_target_label(feature_extractors_ls,
@ -88,13 +84,9 @@ def parse_arguments(argv):
help='name of dataset', default='scrub')
parser.add_argument('--feature-extractor', type=str,
help="name of the feature extractor used for optimization",
default="../feature_extractors/webface_dense_robust_extract.h5")
parser.add_argument('--layer-idx', type=int,
help="the idx of the layer of neuron that are used as feature space",
default=-3)
parser.add_argument('--th', type=float, default=0.01)
parser.add_argument('--sd', type=int, default=1e4)
default="webface_dense_robust")
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)

60
fawkes/utils.py
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@ -68,7 +68,6 @@ def init_gpu(gpu_index, force=False):
def preprocess(X, method):
# assume color last
assert method in {'raw', 'imagenet', 'inception', 'mnist'}
if method is 'raw':
@ -82,7 +81,6 @@ def preprocess(X, method):
def reverse_preprocess(X, method):
# assume color last
assert method in {'raw', 'imagenet', 'inception', 'mnist'}
if method is 'raw':
@ -146,13 +144,6 @@ def imagenet_preprocessing(x, data_format=None):
def imagenet_reverse_preprocessing(x, data_format=None):
import keras.backend as K
""" Reverse preprocesses a tensor encoding a batch of images.
# Arguments
x: input Numpy tensor, 4D.
data_format: data format of the image tensor.
# Returns
Preprocessed tensor.
"""
x = np.array(x)
if data_format is None:
data_format = K.image_data_format()
@ -189,37 +180,32 @@ def build_bottleneck_model(model, cut_off):
return bottleneck_model
def load_extractor(name, layer_idx=None):
model = keras.models.load_model(name)
if "extract" in name.split("/")[-1]:
model = keras.models.load_model(name)
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")
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 dataset == "scrub":
train_data_dir = '../data/scrub/train'
test_data_dir = '../data/scrub/test'
number_classes = 530
number_samples = 57838
elif dataset == "pubfig":
train_data_dir = '../data/pubfig/train'
test_data_dir = '../data/pubfig/test'
number_classes = 65
number_samples = 5979
else:
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 train_data_dir, test_data_dir, number_classes, number_samples
return config[dataset]['train_dir'], config[dataset]['test_dir'], config[dataset]['num_classes'], config[dataset][
'num_images']
def normalize(x):
@ -227,10 +213,9 @@ def normalize(x):
class CloakData(object):
def __init__(self, dataset, img_shape=(224, 224), target_selection_tries=30, protect_class=None):
def __init__(self, dataset, img_shape=(224, 224), protect_class=None):
self.dataset = dataset
self.img_shape = img_shape
self.target_selection_tries = target_selection_tries
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)))
@ -269,7 +254,6 @@ class CloakData(object):
def load_embeddings(self, feature_extractors_names):
dictionaries = []
for extractor_name in feature_extractors_names:
extractor_name = extractor_name.split("/")[-1].split('.')[0].replace("_extract", "")
path2emb = pickle.load(open("../feature_extractors/embeddings/{}_emb_norm.p".format(extractor_name), "rb"))
dictionaries.append(path2emb)
@ -288,14 +272,14 @@ class CloakData(object):
embs = [p[1] for p in items]
embs = np.array(embs)
pair_dist = pairwise_distances(original_feature_x, embs, 'l2')
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[:2]:
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])