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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2020-05-17
# @Author : Shawn Shan (shansixiong@cs.uchicago.edu)
# @Link : https://www.shawnshan.com/
import errno
import glob
import gzip
import hashlib
import json
import os
import pickle
import random
import shutil
import sys
import tarfile
import zipfile
import PIL
import pkg_resources
import six
from keras.utils import Progbar
from six.moves.urllib.error import HTTPError, URLError
stderr = sys.stderr
sys.stderr = open(os.devnull, 'w')
import keras
sys.stderr = stderr
import keras.backend as K
import numpy as np
import tensorflow as tf
from PIL import Image, ExifTags
from keras.layers import Dense, Activation
from keras.models import Model
from keras.preprocessing import image
from fawkes.align_face import align
from six.moves.urllib.request import urlopen
if sys.version_info[0] == 2:
def urlretrieve(url, filename, reporthook=None, data=None):
def chunk_read(response, chunk_size=8192, reporthook=None):
content_type = response.info().get('Content-Length')
total_size = -1
if content_type is not None:
total_size = int(content_type.strip())
count = 0
while True:
chunk = response.read(chunk_size)
count += 1
if reporthook is not None:
reporthook(count, chunk_size, total_size)
if chunk:
yield chunk
else:
break
response = urlopen(url, data)
with open(filename, 'wb') as fd:
for chunk in chunk_read(response, reporthook=reporthook):
fd.write(chunk)
else:
from six.moves.urllib.request import urlretrieve
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
IMG_SIZE = 112
PREPROCESS = 'raw'
def load_image(path):
try:
img = Image.open(path)
except PIL.UnidentifiedImageError:
return None
except IsADirectoryError:
return None
try:
info = img._getexif()
except OSError:
return None
if info is not None:
for orientation in ExifTags.TAGS.keys():
if ExifTags.TAGS[orientation] == 'Orientation':
break
exif = dict(img._getexif().items())
if orientation in exif.keys():
if exif[orientation] == 3:
img = img.rotate(180, expand=True)
elif exif[orientation] == 6:
img = img.rotate(270, expand=True)
elif exif[orientation] == 8:
img = img.rotate(90, expand=True)
else:
pass
img = img.convert('RGB')
image_array = image.img_to_array(img)
return image_array
class Faces(object):
def __init__(self, image, aligner, verbose=1, eval_local=False, preprocessing=True, no_align=False):
self.verbose = verbose
self.no_align = no_align
self.aligner = aligner
self.margin = 30
self.org_faces = [image] # single image in a list
self.cropped_faces = []
self.cropped_faces_shape = []
self.cropped_index = []
self.start_end_ls = []
self.callback_idx = []
self.images_without_face = []
# Processing the single image
cur_img = np.array(image)
if not self.no_align:
align_img = align(cur_img, self.aligner)
if align_img is None:
if self.verbose:
print("Find 0 face(s) in the image")
self.images_without_face.append(0)
return
cur_faces = align_img[0]
else:
cur_faces = [cur_img]
cur_faces = [face for face in cur_faces if face.shape[0] != 0 and face.shape[1] != 0]
cur_shapes = [f.shape[:-1] for f in cur_faces]
cur_faces_square = []
if self.verbose and not self.no_align:
print("Find {} face(s) in the image".format(len(cur_faces)))
if eval_local:
cur_faces = cur_faces[:1]
for img in cur_faces:
if eval_local:
base = resize(img, (IMG_SIZE, IMG_SIZE))
else:
long_size = max([img.shape[1], img.shape[0]]) + self.margin
base = np.ones((long_size, long_size, 3)) * np.mean(img, axis=(0, 1))
start1, end1 = get_ends(long_size, img.shape[0])
start2, end2 = get_ends(long_size, img.shape[1])
base[start1:end1, start2:end2, :] = img
cur_start_end = (start1, end1, start2, end2)
self.start_end_ls.append(cur_start_end)
cur_faces_square.append(base)
cur_faces_square = [resize(f, (IMG_SIZE, IMG_SIZE)) for f in cur_faces_square]
self.cropped_faces.extend(cur_faces_square)
if not self.no_align:
cur_index = align_img[1]
self.cropped_faces_shape.extend(cur_shapes)
self.cropped_index.extend(cur_index[:len(cur_faces_square)])
self.callback_idx.extend([0]*len(cur_faces_square))
if len(self.cropped_faces) == 0:
return
self.cropped_faces = np.array(self.cropped_faces)
if preprocessing:
self.cropped_faces = preprocess(self.cropped_faces, PREPROCESS)
self.cloaked_cropped_faces = None
self.cloaked_faces = np.copy(self.org_faces)
def get_faces(self):
return self.cropped_faces
def merge_faces(self, protected_images, original_images):
if self.no_align:
return np.clip(protected_images, 0.0, 255.0), self.images_without_face
self.cloaked_faces = np.copy(self.org_faces)
for i in range(len(self.cropped_faces)):
cur_protected = protected_images[i]
cur_original = original_images[i]
org_shape = self.cropped_faces_shape[i]
old_square_shape = max([org_shape[0], org_shape[1]]) + self.margin
cur_protected = resize(cur_protected, (old_square_shape, old_square_shape))
cur_original = resize(cur_original, (old_square_shape, old_square_shape))
start1, end1, start2, end2 = self.start_end_ls[i]
reshape_cloak = cur_protected - cur_original
reshape_cloak = reshape_cloak[start1:end1, start2:end2, :]
callback_id = self.callback_idx[i]
bb = self.cropped_index[i]
self.cloaked_faces[callback_id][bb[0]:bb[2], bb[1]:bb[3], :] += reshape_cloak.astype(np.uint8)
for i in range(0, len(self.cloaked_faces)):
self.cloaked_faces[i] = np.clip(self.cloaked_faces[i], 0.0, 255.0)
return self.cloaked_faces, self.images_without_face
def get_ends(longsize, window):
start = (longsize - window) // 2
end = start + window
return start, end
def dump_dictionary_as_json(dict, outfile):
j = json.dumps(dict)
with open(outfile, "wb") as f:
f.write(j.encode())
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 resize(img, sz):
assert np.min(img) >= 0 and np.max(img) <= 255.0
from keras.preprocessing import image
im_data = image.array_to_img(img).resize((sz[1], sz[0]))
im_data = image.img_to_array(im_data)
return im_data
def init_gpu(gpu):
''' code to initialize gpu in tf2'''
if isinstance(gpu, list):
gpu_num = ','.join([str(i) for i in gpu])
else:
gpu_num = str(gpu)
if "CUDA_VISIBLE_DEVICES" in os.environ:
print('GPU already initiated')
return
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_num
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
except RuntimeError as e:
print(e)
def fix_gpu_memory(mem_fraction=1):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf_config = None
if tf.test.is_gpu_available():
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 preprocess(X, method):
assert method in {'raw', 'imagenet', 'inception', 'mnist'}
if method == 'raw':
pass
elif method == '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 == 'raw':
pass
elif method == '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 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',
optimizer='adam',
metrics=['accuracy'])
return bottleneck_model
def load_extractor(name):
hash_map = {"extractor_2": "ce703d481db2b83513bbdafa27434703",
"extractor_0": "94854151fd9077997d69ceda107f9c6b"}
assert name in ["extractor_2", 'extractor_0']
model_file = pkg_resources.resource_filename("fawkes", "model/{}.h5".format(name))
cur_hash = hash_map[name]
model_dir = pkg_resources.resource_filename("fawkes", "model/")
os.makedirs(model_dir, exist_ok=True)
get_file("{}.h5".format(name), "http://mirror.cs.uchicago.edu/fawkes/files/{}.h5".format(name),
cache_dir=model_dir, cache_subdir='', md5_hash=cur_hash)
model = keras.models.load_model(model_file)
model = Extractor(model)
return model
class Extractor(object):
def __init__(self, model):
self.model = model
def predict(self, imgs):
imgs = imgs / 255.0
embeds = l2_norm(self.model(imgs))
return embeds
def __call__(self, x):
return self.predict(x)
def get_dataset_path(dataset):
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
if not os.path.exists(os.path.join(model_dir, "config.json")):
raise Exception("Please config the datasets before running protection code. See more in README and config.py.")
config = json.load(open(os.path.join(model_dir, "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 dump_image(x, filename, format="png", scale=False):
img = image.array_to_img(x, scale=scale)
img.save(filename, format)
return
def load_embeddings(feature_extractors_names):
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
for extractor_name in feature_extractors_names:
fp = gzip.open(os.path.join(model_dir, "{}_emb.p.gz".format(extractor_name)), 'rb')
path2emb = pickle.load(fp)
fp.close()
return path2emb
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)
return concated_feature_ls
def pairwise_l2_distance(A, B):
BT = B.transpose()
vecProd = np.dot(A, BT)
SqA = A ** 2
sumSqA = np.matrix(np.sum(SqA, axis=1))
sumSqAEx = np.tile(sumSqA.transpose(), (1, vecProd.shape[1]))
SqB = B ** 2
sumSqB = np.sum(SqB, axis=1)
sumSqBEx = np.tile(sumSqB, (vecProd.shape[0], 1))
SqED = sumSqBEx + sumSqAEx - 2 * vecProd
SqED[SqED < 0] = 0.0
ED = np.sqrt(SqED)
return ED
def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, metric='l2'):
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
original_feature_x = extractor_ls_predict(feature_extractors_ls, imgs)
path2emb = load_embeddings(feature_extractors_names)
items = list([(k, v) for k, v in path2emb.items()])
paths = [p[0] for p in items]
embs = [p[1] for p in items]
embs = np.array(embs)
pair_dist = pairwise_l2_distance(original_feature_x, embs)
pair_dist = np.array(pair_dist)
max_sum = np.min(pair_dist, axis=0)
max_id_ls = np.argsort(max_sum)[::-1]
max_id = random.choice(max_id_ls[:20])
target_data_id = paths[int(max_id)]
print("target ID: {}".format(target_data_id))
image_dir = os.path.join(model_dir, "target_data/{}".format(target_data_id))
os.makedirs(os.path.join(model_dir, "target_data"), exist_ok=True)
os.makedirs(image_dir, exist_ok=True)
for i in range(10):
if os.path.exists(os.path.join(model_dir, "target_data/{}/{}.jpg".format(target_data_id, i))):
continue
try:
get_file("{}.jpg".format(i),
"http://mirror.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg".format(target_data_id, i),
cache_dir=model_dir, cache_subdir='target_data/{}/'.format(target_data_id))
except Exception:
pass
image_paths = glob.glob(image_dir + "/*.jpg")
target_images = [image.img_to_array(image.load_img(cur_path)) for cur_path in
image_paths]
target_images = np.array([resize(x, (IMG_SIZE, IMG_SIZE)) for x in target_images])
target_images = preprocess(target_images, PREPROCESS)
target_images = list(target_images)
while len(target_images) < len(imgs):
target_images += target_images
target_images = random.sample(target_images, len(imgs))
return np.array(target_images)
def l2_norm(x, axis=1):
"""l2 norm"""
norm = tf.norm(x, axis=axis, keepdims=True)
output = x / norm
return output
""" TensorFlow implementation get_file
https://github.com/tensorflow/tensorflow/blob/v2.3.0/tensorflow/python/keras/utils/data_utils.py#L168-L297
"""
def get_file(fname,
origin,
untar=False,
md5_hash=None,
file_hash=None,
cache_subdir='datasets',
hash_algorithm='auto',
extract=False,
archive_format='auto',
cache_dir=None):
if cache_dir is None:
cache_dir = os.path.join(os.path.expanduser('~'), '.keras')
if md5_hash is not None and file_hash is None:
file_hash = md5_hash
hash_algorithm = 'md5'
datadir_base = os.path.expanduser(cache_dir)
if not os.access(datadir_base, os.W_OK):
datadir_base = os.path.join('/tmp', '.keras')
datadir = os.path.join(datadir_base, cache_subdir)
_makedirs_exist_ok(datadir)
# fname = path_to_string(fname)
if untar:
untar_fpath = os.path.join(datadir, fname)
fpath = untar_fpath + '.tar.gz'
else:
fpath = os.path.join(datadir, fname)
download = False
if os.path.exists(fpath):
# File found; verify integrity if a hash was provided.
if file_hash is not None:
if not validate_file(fpath, file_hash, algorithm=hash_algorithm):
print('A local file was found, but it seems to be '
'incomplete or outdated because the ' + hash_algorithm +
' file hash does not match the original value of ' + file_hash +
' so we will re-download the data.')
download = True
else:
download = True
if download:
print('Downloading data from', origin)
class ProgressTracker(object):
# Maintain progbar for the lifetime of download.
# This design was chosen for Python 2.7 compatibility.
progbar = None
def dl_progress(count, block_size, total_size):
if ProgressTracker.progbar is None:
if total_size == -1:
total_size = None
ProgressTracker.progbar = Progbar(total_size)
else:
ProgressTracker.progbar.update(count * block_size)
error_msg = 'URL fetch failure on {}: {} -- {}'
try:
try:
urlretrieve(origin, fpath, dl_progress)
except HTTPError as e:
raise Exception(error_msg.format(origin, e.code, e.msg))
except URLError as e:
raise Exception(error_msg.format(origin, e.errno, e.reason))
except (Exception, KeyboardInterrupt) as e:
if os.path.exists(fpath):
os.remove(fpath)
raise
ProgressTracker.progbar = None
if untar:
if not os.path.exists(untar_fpath):
_extract_archive(fpath, datadir, archive_format='tar')
return untar_fpath
if extract:
_extract_archive(fpath, datadir, archive_format)
return fpath
def _extract_archive(file_path, path='.', archive_format='auto'):
if archive_format is None:
return False
if archive_format == 'auto':
archive_format = ['tar', 'zip']
if isinstance(archive_format, six.string_types):
archive_format = [archive_format]
for archive_type in archive_format:
if archive_type == 'tar':
open_fn = tarfile.open
is_match_fn = tarfile.is_tarfile
if archive_type == 'zip':
open_fn = zipfile.ZipFile
is_match_fn = zipfile.is_zipfile
if is_match_fn(file_path):
with open_fn(file_path) as archive:
try:
archive.extractall(path)
except (tarfile.TarError, RuntimeError, KeyboardInterrupt):
if os.path.exists(path):
if os.path.isfile(path):
os.remove(path)
else:
shutil.rmtree(path)
raise
return True
return False
def _makedirs_exist_ok(datadir):
if six.PY2:
# Python 2 doesn't have the exist_ok arg, so we try-except here.
try:
os.makedirs(datadir)
except OSError as e:
if e.errno != errno.EEXIST:
raise
else:
os.makedirs(datadir, exist_ok=True) # pylint: disable=unexpected-keyword-arg
def validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535):
"""Validates a file against a sha256 or md5 hash.
Arguments:
fpath: path to the file being validated
file_hash: The expected hash string of the file.
The sha256 and md5 hash algorithms are both supported.
algorithm: Hash algorithm, one of 'auto', 'sha256', or 'md5'.
The default 'auto' detects the hash algorithm in use.
chunk_size: Bytes to read at a time, important for large files.
Returns:
Whether the file is valid
"""
if (algorithm == 'sha256') or (algorithm == 'auto' and len(file_hash) == 64):
hasher = 'sha256'
else:
hasher = 'md5'
if str(_hash_file(fpath, hasher, chunk_size)) == str(file_hash):
return True
else:
return False
def _hash_file(fpath, algorithm='sha256', chunk_size=65535):
"""Calculates a file sha256 or md5 hash.
Example:
```python
_hash_file('/path/to/file.zip')
'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855'
```
Arguments:
fpath: path to the file being validated
algorithm: hash algorithm, one of `'auto'`, `'sha256'`, or `'md5'`.
The default `'auto'` detects the hash algorithm in use.
chunk_size: Bytes to read at a time, important for large files.
Returns:
The file hash
"""
if (algorithm == 'sha256') or (algorithm == 'auto' and len(hash) == 64):
hasher = hashlib.sha256()
else:
hasher = hashlib.md5()
with open(fpath, 'rb') as fpath_file:
for chunk in iter(lambda: fpath_file.read(chunk_size), b''):
hasher.update(chunk)
return hasher.hexdigest()
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