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https://github.com/Shawn-Shan/fawkes.git
synced 2024-09-20 07:26:37 +05:30
improve performance
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Shawn-Shan
parent
ef85fd5c53
commit
2fd86380bb
@ -4,7 +4,7 @@
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# @Link : https://www.shawnshan.com/
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__version__ = '0.0.9'
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__version__ = '0.1.0'
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from .detect_faces import create_mtcnn, run_detect_face
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from .differentiator import FawkesMaskGeneration
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@ -48,7 +48,7 @@ class FawkesMaskGeneration:
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max_iterations=MAX_ITERATIONS, initial_const=INITIAL_CONST,
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intensity_range=INTENSITY_RANGE, l_threshold=L_THRESHOLD,
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max_val=MAX_VAL, keep_final=KEEP_FINAL, maximize=MAXIMIZE, image_shape=IMAGE_SHAPE,
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verbose=0, ratio=RATIO, limit_dist=LIMIT_DIST, faces=None):
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verbose=0, ratio=RATIO, limit_dist=LIMIT_DIST):
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assert intensity_range in {'raw', 'imagenet', 'inception', 'mnist'}
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@ -70,7 +70,6 @@ class FawkesMaskGeneration:
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self.ratio = ratio
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self.limit_dist = limit_dist
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self.single_shape = list(image_shape)
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self.faces = faces
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self.input_shape = tuple([self.batch_size] + self.single_shape)
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@ -168,7 +167,8 @@ class FawkesMaskGeneration:
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self.bottlesim_sum = 0.0
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self.bottlesim_push = 0.0
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for bottleneck_model in bottleneck_model_ls:
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model_input_shape = bottleneck_model.input_shape[1:]
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model_input_shape = (224, 224, 3)
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cur_aimg_input = resize_tensor(self.aimg_input, model_input_shape)
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self.bottleneck_a = bottleneck_model(cur_aimg_input)
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@ -267,7 +267,7 @@ class FawkesMaskGeneration:
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% int(np.ceil(len(source_imgs) / self.batch_size)))
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for idx in range(0, len(source_imgs), self.batch_size):
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print('processing batch %d at %s' % (idx, datetime.datetime.now()))
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print('processing image %d at %s' % (idx+1, datetime.datetime.now()))
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adv_img = self.attack_batch(source_imgs[idx:idx + self.batch_size],
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target_imgs[idx:idx + self.batch_size],
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weights[idx:idx + self.batch_size])
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@ -315,8 +315,6 @@ 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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temp_images = []
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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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@ -407,8 +405,9 @@ class FawkesMaskGeneration:
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if all_clear:
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break
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if iteration != 0 and iteration % (self.MAX_ITERATIONS // 2) == 0:
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if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0:
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LR = LR * 0.8
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print("LR: {}".format(LR))
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if iteration % (self.MAX_ITERATIONS // 5) == 0:
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if self.verbose == 1:
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@ -6,9 +6,7 @@ import argparse
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import glob
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import logging
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import os
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import random
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import sys
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import time
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import tensorflow as tf
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@ -22,26 +20,9 @@ from fawkes.utils import load_extractor, init_gpu, select_target_label, dump_ima
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from fawkes.align_face import aligner
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from fawkes.utils import get_file
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random.seed(12243)
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np.random.seed(122412)
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def generate_cloak_images(sess, feature_extractors, image_X, target_emb=None, th=0.01, faces=None, sd=1e9, lr=2,
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max_step=500, batch_size=1, debug=False):
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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=sd,
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learning_rate=lr,
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max_iterations=max_step,
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l_threshold=th,
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verbose=1 if debug else 0, maximize=False, keep_final=False, image_shape=image_X.shape[1:],
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faces=faces)
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cloaked_image_X = differentiator.attack(image_X, target_emb)
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def generate_cloak_images(protector, image_X, target_emb=None):
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cloaked_image_X = protector.attack(image_X, target_emb)
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return cloaked_image_X
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@ -79,6 +60,9 @@ class Fawkes(object):
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self.aligner = aligner(sess)
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self.feature_extractors_ls = [load_extractor(name) for name in self.fs_names]
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self.protector = None
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self.protector_param = None
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def mode2param(self, mode):
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if mode == 'low':
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th = 0.003
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@ -86,12 +70,12 @@ class Fawkes(object):
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lr = 20
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elif mode == 'mid':
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th = 0.005
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max_step = 100
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lr = 20
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max_step = 120
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lr = 15
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elif mode == 'high':
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th = 0.008
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max_step = 200
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lr = 20
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max_step = 600
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lr = 10
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elif mode == 'ultra':
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if not tf.test.is_gpu_available():
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print("Please enable GPU for ultra setting...")
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@ -103,7 +87,7 @@ class Fawkes(object):
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raise Exception("mode must be one of 'low', 'mid', 'high', 'ultra', 'custom'")
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return th, max_step, lr
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def run_protection(self, image_paths, mode='mid', th=0.04, sd=1e9, lr=10, max_step=500, batch_size=1, format='png',
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def run_protection(self, image_paths, mode='low', th=0.04, sd=1e9, lr=10, max_step=500, batch_size=1, format='png',
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separate_target=True, debug=False):
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if mode == 'custom':
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@ -111,6 +95,9 @@ class Fawkes(object):
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else:
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th, max_step, lr = self.mode2param(mode)
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current_param = "-".join([str(x) for x in [mode, th, sd, lr, max_step, batch_size, format,
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separate_target, debug]])
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image_paths, loaded_images = filter_image_paths(image_paths)
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if not image_paths:
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@ -132,9 +119,27 @@ class Fawkes(object):
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else:
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target_embedding = select_target_label(original_images, self.feature_extractors_ls, self.fs_names)
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protected_images = generate_cloak_images(sess, self.feature_extractors_ls, original_images,
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target_emb=target_embedding, th=th, faces=faces, sd=sd,
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lr=lr, max_step=max_step, batch_size=batch_size, debug=debug)
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if current_param != self.protector_param:
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self.protector_param = current_param
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if self.protector is not None:
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del self.protector
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self.protector = FawkesMaskGeneration(sess, self.feature_extractors_ls,
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batch_size=batch_size,
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mimic_img=True,
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intensity_range='imagenet',
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initial_const=sd,
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learning_rate=lr,
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max_iterations=max_step,
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l_threshold=th,
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verbose=1 if debug else 0,
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maximize=False,
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keep_final=False,
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image_shape=(224, 224, 3))
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protected_images = generate_cloak_images(self.protector, original_images,
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target_emb=target_embedding)
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faces.cloaked_cropped_faces = protected_images
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@ -145,7 +150,7 @@ class Fawkes(object):
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for p_img, path in zip(final_images, image_paths):
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file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), mode, format)
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dump_image(p_img, file_name, format=format)
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# elapsed_time = time.time() - start_time
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print("Done!")
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return None
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@ -141,6 +141,9 @@ class Faces(object):
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if verbose:
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print("Find {} face(s) in {}".format(len(cur_faces), p.split("/")[-1]))
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if eval_local:
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cur_faces = cur_faces[:1]
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for img in cur_faces:
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if eval_local:
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base = resize(img, (224, 224))
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@ -150,6 +153,7 @@ class Faces(object):
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base[0:img.shape[0], 0:img.shape[1], :] = img
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cur_faces_square.append(base)
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cur_index = align_img[1]
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cur_faces_square = [resize(f, (224, 224)) for f in cur_faces_square]
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self.cropped_faces_shape.extend(cur_shapes)
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6
setup.py
6
setup.py
@ -77,7 +77,7 @@ setup_requires = []
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install_requires = [
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'numpy==1.16.4',
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# 'tensorflow-gpu>=1.13.1, <=1.14.0',
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'tensorflow>=1.12.0, <=1.15.0',
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'tensorflow>=1.12.0, <=1.15.0', # change this is tensorflow-gpu if using GPU machine.
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'argparse',
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'keras>=2.2.5, <=2.3.1',
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'scikit-image',
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@ -88,7 +88,7 @@ install_requires = [
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setup(
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name='fawkes',
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version=__version__,
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license='MIT',
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license='BSD',
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description='An utility to protect user privacy',
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long_description=long_description,
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long_description_content_type='text/markdown',
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@ -114,5 +114,5 @@ setup(
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},
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include_package_data=True,
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zip_safe=False,
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python_requires='>=3.5',
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python_requires='>=3.5,<3.8',
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)
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