kopia lustrzana https://github.com/Shawn-Shan/fawkes
Remove depreciated code and clean up imports
Jacob Logas
rodzic
aedaa82d22
commit
953a473903
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@ -6,6 +6,7 @@ build/*
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keras/datasets/data/*
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keras/datasets/temp/*
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tags
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fawkes/model/
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fawkes.egg-info
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# test-related
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@ -17,4 +18,6 @@ fawkes_dev/protect_personId.txt
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# developer environments
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.idea
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.vscode
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.vscode
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photos/*
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@ -8,13 +8,12 @@ __version__ = '1.0.2'
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from .differentiator import FawkesMaskGeneration
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from .protection import main, Fawkes
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from .utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, Faces, get_file, \
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from .utils import load_extractor, init_gpu, dump_image, reverse_process_cloaked, Faces, get_file, \
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filter_image_paths
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__all__ = (
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'__version__',
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'FawkesMaskGeneration', 'load_extractor',
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'init_gpu',
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'select_target_label', 'dump_image', 'reverse_process_cloaked',
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'init_gpu', 'dump_image', 'reverse_process_cloaked',
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'Faces', 'get_file', 'filter_image_paths', 'main', 'Fawkes'
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)
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@ -8,6 +8,7 @@ import time
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import numpy as np
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import tensorflow as tf
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from tensorflow.keras.optimizers.legacy import Adadelta
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from fawkes.utils import preprocess, reverse_preprocess
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from keras.utils import Progbar
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@ -146,9 +147,9 @@ class FawkesMaskGeneration:
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def compute_feature_loss(self, tape, aimg_raw, simg_raw, aimg_input, timg_input, simg_input):
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""" Compute input space + feature space loss.
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"""
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input_space_loss, dist_raw, input_space_loss_sum, input_space_loss_raw_avg = self.calc_dissim(aimg_raw,
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input_space_loss, dist_raw, _, input_space_loss_raw_avg = self.calc_dissim(aimg_raw,
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simg_raw)
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feature_space_loss, feature_space_loss_sum = self.calc_bottlesim(tape, aimg_input, timg_input, simg_input)
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feature_space_loss, _ = self.calc_bottlesim(tape, aimg_input, timg_input, simg_input)
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if self.maximize:
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loss = self.const * tf.square(input_space_loss) - feature_space_loss * self.const_diff
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@ -172,7 +173,7 @@ class FawkesMaskGeneration:
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print('protection cost %f s' % elapsed_time)
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return np.array(adv_imgs)
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def compute_batch(self, source_imgs, target_imgs=None, retry=True):
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def compute_batch(self, source_imgs, target_imgs=None):
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""" TF2 method to generate the cloak. """
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# preprocess images.
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global progressbar
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@ -195,7 +196,7 @@ class FawkesMaskGeneration:
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dtype=tf.float32)
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# make the optimizer
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optimizer = tf.keras.optimizers.Adadelta(float(self.learning_rate))
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optimizer = Adadelta(float(self.learning_rate))
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const_numpy = np.ones(len(source_imgs)) * self.initial_const
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self.const = tf.Variable(const_numpy, dtype=np.float32)
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@ -79,7 +79,7 @@ class Fawkes(object):
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return th, max_step, lr, extractors
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def run_protection(self, image_paths, th=0.04, sd=1e7, lr=10, max_step=500, batch_size=1, format='png',
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separate_target=True, debug=False, no_align=False, exp="", maximize=True,
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separate_target=True, debug=False, no_align=False, maximize=True,
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save_last_on_failed=True):
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current_param = "-".join([str(x) for x in [self.th, sd, self.lr, self.max_step, batch_size, format,
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@ -122,7 +122,6 @@ class Fawkes(object):
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save_last_on_failed=save_last_on_failed,
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)
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protected_images = generate_cloak_images(self.protector, original_images)
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faces.cloaked_cropped_faces = protected_images
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final_images, images_without_face = faces.merge_faces(
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reverse_process_cloaked(protected_images, preprocess=PREPROCESS),
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144
fawkes/utils.py
144
fawkes/utils.py
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@ -9,7 +9,6 @@ import errno
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import glob
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import gzip
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import hashlib
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import json
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import os
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import pickle
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import random
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@ -18,7 +17,6 @@ import sys
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import tarfile
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import zipfile
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import PIL
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import pkg_resources
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import six
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from keras.utils import Progbar
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@ -26,16 +24,14 @@ from six.moves.urllib.error import HTTPError, URLError
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stderr = sys.stderr
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sys.stderr = open(os.devnull, 'w')
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import keras
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from tensorflow.keras.models import load_model
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sys.stderr = stderr
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import keras.backend as K
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from tensorflow.keras.backend import image_data_format
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import numpy as np
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import tensorflow as tf
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from PIL import Image, ExifTags
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from keras.layers import Dense, Activation
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from keras.models import Model
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from keras.preprocessing import image
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from PIL import Image, ExifTags, UnidentifiedImageError
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from tensorflow.keras.preprocessing import image
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from fawkes.align_face import align
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from six.moves.urllib.request import urlopen
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@ -65,14 +61,6 @@ if sys.version_info[0] == 2:
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else:
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from six.moves.urllib.request import urlretrieve
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def clip_img(X, preprocessing='raw'):
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X = reverse_preprocess(X, preprocessing)
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X = np.clip(X, 0.0, 255.0)
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X = preprocess(X, preprocessing)
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return X
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IMG_SIZE = 112
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PREPROCESS = 'raw'
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@ -80,7 +68,7 @@ PREPROCESS = 'raw'
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def load_image(path):
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try:
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img = Image.open(path)
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except PIL.UnidentifiedImageError:
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except UnidentifiedImageError:
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return None
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except IsADirectoryError:
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return None
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@ -199,12 +187,8 @@ class Faces(object):
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if preprocessing:
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self.cropped_faces = preprocess(self.cropped_faces, PREPROCESS)
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self.cloaked_cropped_faces = None
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self.cloaked_faces = np.copy(self.org_faces)
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def get_faces(self):
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return self.cropped_faces
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def merge_faces(self, protected_images, original_images):
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if self.no_align:
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return np.clip(protected_images, 0.0, 255.0), self.images_without_face
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@ -242,25 +226,6 @@ def get_ends(longsize, window):
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return start, end
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def dump_dictionary_as_json(dict, outfile):
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j = json.dumps(dict)
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with open(outfile, "wb") as f:
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f.write(j.encode())
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def load_victim_model(number_classes, teacher_model=None, end2end=False):
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for l in teacher_model.layers:
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l.trainable = end2end
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x = teacher_model.layers[-1].output
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x = Dense(number_classes)(x)
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x = Activation('softmax', name="act")(x)
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model = Model(teacher_model.input, x)
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opt = keras.optimizers.Adadelta()
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model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
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return model
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def resize(img, sz):
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assert np.min(img) >= 0 and np.max(img) <= 255.0
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from keras.preprocessing import image
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@ -290,21 +255,6 @@ def init_gpu(gpu):
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print(e)
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def fix_gpu_memory(mem_fraction=1):
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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tf_config = None
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if tf.test.is_gpu_available():
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gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_fraction)
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tf_config = tf.ConfigProto(gpu_options=gpu_options)
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tf_config.gpu_options.allow_growth = True
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tf_config.log_device_placement = False
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init_op = tf.global_variables_initializer()
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sess = tf.Session(config=tf_config)
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sess.run(init_op)
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K.set_session(sess)
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return sess
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def preprocess(X, method):
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assert method in {'raw', 'imagenet', 'inception', 'mnist'}
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@ -333,7 +283,7 @@ def reverse_preprocess(X, method):
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def imagenet_preprocessing(x, data_format=None):
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if data_format is None:
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data_format = K.image_data_format()
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data_format = image_data_format()
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assert data_format in ('channels_last', 'channels_first')
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x = np.array(x)
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@ -381,10 +331,9 @@ def imagenet_preprocessing(x, data_format=None):
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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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if data_format is None:
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data_format = K.image_data_format()
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data_format = image_data_format()
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assert data_format in ('channels_last', 'channels_first')
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if data_format == 'channels_first':
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@ -411,18 +360,9 @@ def imagenet_reverse_preprocessing(x, data_format=None):
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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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optimizer='adam',
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metrics=['accuracy'])
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return bottleneck_model
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def load_extractor(name):
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hash_map = {"extractor_2": "ce703d481db2b83513bbdafa27434703",
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"extractor_0": "94854151fd9077997d69ceda107f9c6b"}
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@ -434,7 +374,7 @@ def load_extractor(name):
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get_file("{}.h5".format(name), "http://mirror.cs.uchicago.edu/fawkes/files/{}.h5".format(name),
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cache_dir=model_dir, cache_subdir='', md5_hash=cur_hash)
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model = keras.models.load_model(model_file)
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model = load_model(model_file)
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model = Extractor(model)
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return model
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@ -452,20 +392,6 @@ class Extractor(object):
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return self.predict(x)
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def get_dataset_path(dataset):
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model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
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if not os.path.exists(os.path.join(model_dir, "config.json")):
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raise Exception("Please config the datasets before running protection code. See more in README and config.py.")
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config = json.load(open(os.path.join(model_dir, "config.json"), 'r'))
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if dataset not in config:
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raise Exception(
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"Dataset {} does not exist, please download to data/ and add the path to this function... Abort".format(
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dataset))
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return config[dataset]['train_dir'], config[dataset]['test_dir'], config[dataset]['num_classes'], config[dataset][
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'num_images']
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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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@ -506,60 +432,6 @@ def pairwise_l2_distance(A, B):
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ED = np.sqrt(SqED)
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return ED
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def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, metric='l2'):
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model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
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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([(k, v) for k, v in 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_l2_distance(original_feature_x, embs)
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pair_dist = np.array(pair_dist)
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max_sum = np.min(pair_dist, axis=0)
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max_id_ls = np.argsort(max_sum)[::-1]
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max_id = random.choice(max_id_ls[:20])
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target_data_id = paths[int(max_id)]
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print("target ID: {}".format(target_data_id))
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image_dir = os.path.join(model_dir, "target_data/{}".format(target_data_id))
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os.makedirs(os.path.join(model_dir, "target_data"), exist_ok=True)
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os.makedirs(image_dir, exist_ok=True)
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for i in range(10):
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if os.path.exists(os.path.join(model_dir, "target_data/{}/{}.jpg".format(target_data_id, i))):
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continue
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try:
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get_file("{}.jpg".format(i),
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"http://mirror.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg".format(target_data_id, i),
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cache_dir=model_dir, cache_subdir='target_data/{}/'.format(target_data_id))
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except Exception:
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pass
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image_paths = glob.glob(image_dir + "/*.jpg")
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target_images = [image.img_to_array(image.load_img(cur_path)) for cur_path in
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image_paths]
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target_images = np.array([resize(x, (IMG_SIZE, IMG_SIZE)) for x in target_images])
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target_images = preprocess(target_images, PREPROCESS)
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target_images = list(target_images)
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while len(target_images) < len(imgs):
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target_images += target_images
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target_images = random.sample(target_images, len(imgs))
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return np.array(target_images)
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def l2_norm(x, axis=1):
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"""l2 norm"""
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norm = tf.norm(x, axis=axis, keepdims=True)
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@ -0,0 +1,5 @@
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from fawkes.protection import Fawkes
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import os
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fwks = Fawkes("extractor_2", '0', 1, mode="low")
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fwks.run_protection(['./photos/P2.jpg'], format='jpeg')
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