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protection.py
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protection.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2020-05-17
# @Author : Shawn Shan ([email protected])
# @Link : https://www.shawnshan.com/
import argparse
import glob
import logging
import os
import sys
logging.getLogger('tensorflow').setLevel(logging.ERROR)
os.environ["KMP_AFFINITY"] = "noverbose"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
tf.get_logger().setLevel('ERROR')
tf.autograph.set_verbosity(3)
import numpy as np
from fawkes.differentiator import FawkesMaskGeneration
from fawkes.utils import init_gpu, dump_image, reverse_process_cloaked, \
Faces, filter_image_paths, load_extractor
from fawkes.align_face import aligner
def generate_cloak_images(protector, image_X, target_emb=None):
cloaked_image_X = protector.compute(image_X, target_emb)
return cloaked_image_X
IMG_SIZE = 112
PREPROCESS = 'raw'
class Fawkes(object):
def __init__(self, feature_extractor, gpu, batch_size, mode="low"):
self.feature_extractor = feature_extractor
self.gpu = gpu
self.batch_size = batch_size
self.mode = mode
th, max_step, lr, extractors = self.mode2param(self.mode)
self.th = th
self.lr = lr
self.max_step = max_step
if gpu is not None:
init_gpu(gpu)
self.aligner = aligner()
self.protector = None
self.protector_param = None
self.feature_extractors_ls = [load_extractor(name) for name in extractors]
def mode2param(self, mode):
if mode == 'low':
th = 0.004
max_step = 40
lr = 25
extractors = ["extractor_2"]
elif mode == 'mid':
th = 0.012
max_step = 75
lr = 20
extractors = ["extractor_0", "extractor_2"]
elif mode == 'high':
th = 0.017
max_step = 150
lr = 15
extractors = ["extractor_0", "extractor_2"]
else:
raise Exception("mode must be one of 'min', 'low', 'mid', 'high'")
return th, max_step, lr, extractors
def run_protection(self, image_paths, th=0.04, sd=1e7, lr=10, max_step=500, batch_size=1, format='png',
separate_target=True, debug=False, no_align=False, exp="", maximize=True,
save_last_on_failed=True):
current_param = "-".join([str(x) for x in [self.th, sd, self.lr, self.max_step, batch_size, format,
separate_target, debug]])
image_paths, loaded_images = filter_image_paths(image_paths)
if not image_paths:
print("No images in the directory")
return 3
faces = Faces(image_paths, loaded_images, self.aligner, verbose=1, no_align=no_align)
original_images = faces.cropped_faces
if len(original_images) == 0:
print("No face detected. ")
return 2
original_images = np.array(original_images)
if current_param != self.protector_param:
self.protector_param = current_param
if self.protector is not None:
del self.protector
if batch_size == -1:
batch_size = len(original_images)
self.protector = FawkesMaskGeneration(self.feature_extractors_ls,
batch_size=batch_size,
mimic_img=True,
intensity_range=PREPROCESS,
initial_const=sd,
learning_rate=self.lr,
max_iterations=self.max_step,
l_threshold=self.th,
verbose=debug,
maximize=maximize,
keep_final=False,
image_shape=(IMG_SIZE, IMG_SIZE, 3),
loss_method='features',
tanh_process=True,
save_last_on_failed=save_last_on_failed,
)
protected_images = generate_cloak_images(self.protector, original_images)
faces.cloaked_cropped_faces = protected_images
final_images, images_without_face = faces.merge_faces(
reverse_process_cloaked(protected_images, preprocess=PREPROCESS),
reverse_process_cloaked(original_images, preprocess=PREPROCESS))
for i in range(len(final_images)):
if i in images_without_face:
continue
p_img = final_images[i]
path = image_paths[i]
file_name = "{}_cloaked.{}".format(".".join(path.split(".")[:-1]), format)
dump_image(p_img, file_name, format=format)
print("Done!")
return 1
def main(*argv):
if not argv:
argv = list(sys.argv)
try:
import signal
signal.signal(signal.SIGPIPE, signal.SIG_DFL)
except Exception as e:
pass
parser = argparse.ArgumentParser()
parser.add_argument('--directory', '-d', type=str,
help='the directory that contains images to run protection', default='imgs/')
parser.add_argument('--gpu', '-g', type=str,
help='the GPU id when using GPU for optimization', default='0')
parser.add_argument('--mode', '-m', type=str,
help='cloak generation mode, select from min, low, mid, high. The higher the mode is, '
'the more perturbation added and stronger protection',
default='low')
parser.add_argument('--feature-extractor', type=str,
help="name of the feature extractor used for optimization",
default="arcface_extractor_0")
parser.add_argument('--th', help='only relevant with mode=custom, DSSIM threshold for perturbation', type=float,
default=0.01)
parser.add_argument('--max-step', help='only relevant with mode=custom, number of steps for optimization', type=int,
default=1000)
parser.add_argument('--sd', type=int, help='only relevant with mode=custom, penalty number, read more in the paper',
default=1e6)
parser.add_argument('--lr', type=float, help='only relevant with mode=custom, learning rate', default=2)
parser.add_argument('--batch-size', help="number of images to run optimization together", type=int, default=1)
parser.add_argument('--separate_target', help="whether select separate targets for each faces in the directory",
action='store_true')
parser.add_argument('--no-align', help="whether to detect and crop faces",
action='store_true')
parser.add_argument('--debug', help="turn on debug and copy/paste the stdout when reporting an issue on github",
action='store_true')
parser.add_argument('--format', type=str,
help="format of the output image",
default="png")
args = parser.parse_args(argv[1:])
assert args.format in ['png', 'jpg', 'jpeg']
if args.format == 'jpg':
args.format = 'jpeg'
image_paths = glob.glob(os.path.join(args.directory, "*"))
image_paths = [path for path in image_paths if "_cloaked" not in path.split("/")[-1]]
protector = Fawkes(args.feature_extractor, args.gpu, args.batch_size, mode=args.mode)
protector.run_protection(image_paths, th=args.th, sd=args.sd, lr=args.lr,
max_step=args.max_step,
batch_size=args.batch_size, format=args.format,
separate_target=args.separate_target, debug=args.debug, no_align=args.no_align)
if __name__ == '__main__':
main(*sys.argv)