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local evaluation code
Former-commit-id: 7f9e32ef86adc18f33704b8824367a610df80e69 [formerly 30a0d4935de4da1c762819eab2bfc2e5411ff6a1] Former-commit-id: 157928d16012db8f7470abb7a297c9740860b5b7
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evaluation/eval_local.py
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184
evaluation/eval_local.py
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@ -0,0 +1,184 @@
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import argparse
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import glob
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import os
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import random
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import sys
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import keras
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import numpy as np
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random.seed(1000)
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from fawkes.utils import init_gpu, load_extractor, load_victim_model, get_file, preprocess, Faces
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from keras.preprocessing import image
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from keras.utils import to_categorical
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from fawkes.align_face import aligner
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def select_samples(data_dir):
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all_data_path = []
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for cls in os.listdir(data_dir):
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cls_dir = os.path.join(data_dir, cls)
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for data_path in os.listdir(cls_dir):
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all_data_path.append(os.path.join(cls_dir, data_path))
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return all_data_path
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class DataGenerator(object):
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def __init__(self, original_images, protect_images):
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l = int(len(original_images) * 0.7)
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self.original_images_test = original_images[l:]
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self.protect_images_train = protect_images[:l]
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other_classes = range(0, 20946)
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selected_classes = random.sample(other_classes, args.num_other_classes)
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print("Downloading additional data...")
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model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
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self.id2label = {-1: 0}
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self.id2path = {}
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self.id2pathtest = {}
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idx = 1
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for target_data_id in selected_classes:
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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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self.id2label[target_data_id] = idx
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idx += 1
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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://sandlab.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg".format(target_data_id,
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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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print("error getting http://sandlab.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg".format(
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target_data_id,
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i))
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pass
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all_pathes = glob.glob(os.path.join(model_dir, 'target_data/{}/*.jpg'.format(target_data_id)))
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test_path = random.sample(all_pathes, 2)
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train_path = [p for p in all_pathes if p not in test_path]
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self.id2path[target_data_id] = train_path
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self.id2pathtest[target_data_id] = test_path
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self.num_classes = 1 + len(self.id2path)
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np.random.seed(12345)
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self.all_id = selected_classes + [-1]
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def generate(self, test=False):
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while True:
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batch_X = []
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batch_Y = []
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cur_batch_path = np.random.choice(self.all_id, 32)
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for p in cur_batch_path:
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cur_y = self.id2label[p]
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if test and p == -1:
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continue
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# protect class images in train dataset
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elif p == -1:
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cur_x = random.choice(self.protect_images_train)
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else:
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if test:
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cur_path = random.choice(self.id2pathtest[p])
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else:
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cur_path = random.choice(self.id2path[p])
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im = image.load_img(cur_path, target_size=(224, 224))
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cur_x = image.img_to_array(im)
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cur_x = preprocess(cur_x, 'imagenet')
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batch_X.append(cur_x)
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batch_Y.append(cur_y)
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batch_X = np.array(batch_X)
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batch_Y = to_categorical(np.array(batch_Y), num_classes=self.num_classes)
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yield batch_X, batch_Y
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def test_original(self):
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original_y = to_categorical([0] * len(self.original_images_test), num_classes=self.num_classes)
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return self.original_images_test, original_y
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class CallbackGenerator(keras.callbacks.Callback):
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def __init__(self, original_imgs, protect_imgs, original_y, original_protect_y, test_gen):
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self.original_imgs = original_imgs
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self.protect_imgs = protect_imgs
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self.original_y = original_y
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self.original_protect_y = original_protect_y
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self.test_gen = test_gen
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def on_epoch_end(self, epoch, logs=None):
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_, original_acc = self.model.evaluate(self.original_imgs, self.original_y, verbose=0)
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print("Epoch: {} - Protection Success Rate {:.4f}".format(epoch, 1 - original_acc))
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def main():
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sess = init_gpu(args.gpu)
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ali = aligner(sess)
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print("Build attacker's model")
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image_paths = glob.glob(os.path.join(args.directory, "*"))
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cloak_file_name = "low_cloaked"
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original_image_paths = sorted([path for path in image_paths if "cloaked" not in path.split("/")[-1]])
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protect_image_paths = sorted([path for path in image_paths if cloak_file_name in path.split("/")[-1]])
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original_faces = Faces(original_image_paths, ali, verbose=1, eval_local=True)
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original_faces = original_faces.cropped_faces
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cloaked_faces = Faces(protect_image_paths, ali, verbose=1, eval_local=True)
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cloaked_faces = cloaked_faces.cropped_faces
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if len(original_faces) <= 10:
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raise Exception("Must have more than 10 protected images to run the evaluation")
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num_classes = args.num_other_classes + 1
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datagen = DataGenerator(original_faces, cloaked_faces)
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original_test_X, original_test_Y = datagen.test_original()
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print("{} Training Images | {} Testing Images".format(len(datagen.protect_images_train), len(original_test_X)))
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train_generator = datagen.generate()
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test_generator = datagen.generate(test=True)
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base_model = load_extractor(args.base_model)
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model = load_victim_model(teacher_model=base_model, number_classes=num_classes)
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cb = CallbackGenerator(original_imgs=original_test_X, protect_imgs=cloaked_faces, original_y=original_test_Y,
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original_protect_y=None,
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test_gen=test_generator)
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model.fit_generator(train_generator, steps_per_epoch=num_classes * 10 // 32,
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epochs=args.n_epochs,
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verbose=2,
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callbacks=[cb]
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)
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_, acc_original = model.evaluate(original_test_X, original_test_Y, verbose=0)
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print("Protection Success Rate: {:.4f}".format(1 - acc_original))
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def parse_arguments(argv):
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parser = argparse.ArgumentParser()
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parser.add_argument('--gpu', type=str,
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help='GPU id', default='0')
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parser.add_argument('--dataset', type=str,
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help='name of dataset', default='scrub')
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parser.add_argument('--num_other_classes', type=int,
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help='name of dataset', default=1000)
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parser.add_argument('--directory', '-d', type=str,
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help='name of the cloak result directory', required=True)
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parser.add_argument('--base_model', type=str,
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help='the feature extractor used for tracker model training. ', default='low_extract')
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parser.add_argument('--n_epochs', type=int, default=5)
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return parser.parse_args(argv)
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if __name__ == '__main__':
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args = parse_arguments(sys.argv[1:])
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main()
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@ -8,6 +8,8 @@ import shutil
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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 six
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from six.moves.urllib.error import HTTPError, URLError
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@ -25,7 +27,7 @@ from keras.models import Model
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from keras.preprocessing import image
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from skimage.transform import resize
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from fawkes.align_face import align, aligner
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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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if sys.version_info[0] == 2:
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@ -62,8 +64,10 @@ def clip_img(X, preprocessing='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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return None
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if img._getexif() is not None:
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for orientation in ExifTags.TAGS.keys():
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@ -87,7 +91,7 @@ def load_image(path):
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class Faces(object):
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def __init__(self, image_paths, aligner, verbose=1):
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def __init__(self, image_paths, aligner, verbose=1, eval_local=False):
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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, "mtcnn.p.gz")):
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os.makedirs(model_dir, exist_ok=True)
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@ -105,8 +109,17 @@ class Faces(object):
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print("Identify {} images".format(len(image_paths)))
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for i, p in enumerate(image_paths):
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cur_img = load_image(p)
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if cur_img is None:
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continue
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self.org_faces.append(cur_img)
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align_img = align(cur_img, self.aligner, margin=0.7)
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if eval_local:
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margin = 0
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else:
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margin = 0.7
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align_img = align(cur_img, self.aligner, margin=margin)
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if align_img is None:
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print("Find 0 face(s) in {}".format(p.split("/")[-1]))
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continue
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@ -120,6 +133,9 @@ class Faces(object):
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print("Find {} face(s) in {}".format(len(cur_faces), p.split("/")[-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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else:
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long_size = max([img.shape[1], img.shape[0]])
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base = np.zeros((long_size, long_size, 3))
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base[0:img.shape[0], 0:img.shape[1], :] = img
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@ -170,21 +186,6 @@ def dump_dictionary_as_json(dict, outfile):
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f.write(j.encode())
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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 load_victim_model(number_classes, teacher_model=None, end2end=False, dropout=0):
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for l in teacher_model.layers:
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l.trainable = end2end
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@ -212,6 +213,21 @@ def init_gpu(gpu_index, force=False):
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return sess
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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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@ -1,65 +0,0 @@
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# Fawkes
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Code implementation of the paper "[Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models](https://arxiv.org/pdf/2002.08327.pdf)", at *USENIX Security 2020*.
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### BEFORE YOU RUN OUR CODE
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We appreciate your interest in our work and for trying out our code. We've noticed several cases where incorrect configuration leads to poor performances of protection. If you also observe low detection performance far away from what we presented in the paper, please feel free to open an issue in this repo or contact any of the authors directly. We are more than happy to help you debug your experiment and find out the correct configuration.
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### ABOUT
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This repository contains code implementation of the paper "[Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models](https://arxiv.org/pdf/2002.08327.pdf)", at *USENIX Security 2020*.
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### DEPENDENCIES
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Our code is implemented and tested on Keras with TensorFlow backend. Following packages are used by our code.
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- `keras==2.3.1`
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- `numpy==1.18.4`
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- `tensorflow-gpu==1.13.1`
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Our code is tested on `Python 3.6.8`
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### HOWTO
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#### Download and Config Datasets
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The first step is to download several datasets for protection and target selection.
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1. Download the following dataset to your local machine. After downloading the datasets, restructure it the same way as the FaceScrub dataset downloaded.
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- FaceScrub -- used for protection evaluation (link)
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- VGGFace1 -- used for target select (link)
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- VGGFace2 -- used for target select (link)
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- WebFace -- used for target select (link)
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2. Config datasets
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open `fawkes/config.py` and update the `DATASETS` dictionary with the path to each dataset. Then run `python fawkes/config.py`. Every time the datasets are updated or moved, remember to rerun the command with the updated path.
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3. Calculate embeddings using feature extractor.
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Run `python3 fawkes/prepare_feature_extractor.py --candidate-datasets scrub vggface1 vggface2 webface`. This will calculate and cache the embeddings using the default feature extractor we provide. To use a customized feature extractor, please look at the Advance section at the end.
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#### Generate Cloak for Images
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To generate cloak, run
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`python3 fawkes/protection.py --gpu 0 --dataset scrub --feature-extractor webface_dense_robust_extract`
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For more information about the detailed parameters, please read `fawkes/protection.py`.
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The code will output a directory in `results/` with `cloak_data.p` inside. You can check the cloaked images or inspect the changes in `this notebook`.
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#### Evaluate Cloak Effectiveness
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To evaluate the cloak, run `python3 fawkes/eval_cloak.py --gpu 0 --cloak_data PATH-TO-RESULT-DIRECTORY --transfer_model vggface2_inception_extract`.
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The code will print out the tracker model accuracy on uncloaked/original test images of the protected user, which should be close to 0.
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#### Exisiting Feature extractors
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We shared three different feature extractors under feature_extractors/
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1. low_extract.h5: trained on WebFace dataset with DenseNet architecture.
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2. mid_extract.h5: VGGFace2 dataset with DenseNet architecture. Trained with PGD adversarial training for 5 epochs.
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3. high_extract.h5: WebFace dataset with DenseNet architecture. Trained with PGD adversarial training for 20 epochs.
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4. high2_extract.h5: VGGFace2 dataset with DenseNet architecture. Trained with PGD adversarial training for 20 epochs.
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### Citation
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```
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@inproceedings{shan2020fawkes,
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title={Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models},
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author={Shan, Shawn and Wenger, Emily and Zhang, Jiayun and Li, Huiying and Zheng, Haitao and Zhao, Ben Y},
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booktitle="Proc. of USENIX Security",
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year={2020}
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}
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```
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@ -1,420 +0,0 @@
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import http.client
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import json
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import random
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import time
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import urllib.error
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import urllib.parse
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import urllib.request
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import requests
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# Face API Key and Endpoint
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f = open('api_key.txt', 'r')
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data = f.read().split("\n")
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subscription_key = data[0]
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uri_base = data[1]
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cloak_image_base = 'http://sandlab.cs.uchicago.edu/fawkes/files/cloak/{}_high_cloaked.png'
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original_image_base = 'http://sandlab.cs.uchicago.edu/fawkes/files/cloak/{}.png'
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def test_cloak():
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NUM_TRAIN = 5
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total_idx = range(0, 82)
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TRAIN_RANGE = random.sample(total_idx, NUM_TRAIN)
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TEST_RANGE = random.sample([i for i in total_idx if i not in TRAIN_RANGE], 20)
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personGroupId = 'all'
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# delete_personGroup(personGroupId)
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# create_personGroupId(personGroupId, personGroupId)
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with open("protect_personId.txt", 'r') as f:
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protect_personId = f.read()
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print(protect_personId)
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delete_personGroupPerson(personGroupId, protect_personId)
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protect_personId = create_personId(personGroupId, 'Emily')
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with open("protect_personId.txt", 'w') as f:
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f.write(protect_personId)
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print("Created protect personId: {}".format(protect_personId))
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for idx in TRAIN_RANGE:
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image_url = cloak_image_base.format(idx)
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r = add_persistedFaceId(personGroupId, protect_personId, image_url)
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if r is not None:
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print("Added {}".format(idx))
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else:
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print("Unable to add {}-th image of protect person".format(idx))
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# add other people
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# for idx_person in range(5000, 15000):
|
||||
# personId = create_personId(personGroupId, str(idx_person))
|
||||
# print("Created personId: {}".format(idx_person))
|
||||
# for idx_image in range(10):
|
||||
# image_url = "http://sandlab.cs.uchicago.edu/fawkes/files/target_data/{}/{}.jpg".format(
|
||||
# idx_person, idx_image)
|
||||
# r = add_persistedFaceId(personGroupId, personId, image_url)
|
||||
# if r is not None:
|
||||
# print("Added {}".format(idx_image))
|
||||
# else:
|
||||
# print("Unable to add {}-th image".format(idx_image))
|
||||
|
||||
# train model based on personGroup
|
||||
|
||||
train_personGroup(personGroupId)
|
||||
|
||||
while json.loads(get_trainStatus(personGroupId))['status'] != 'succeeded':
|
||||
time.sleep(2)
|
||||
|
||||
# list_personGroupPerson(personGroupId)
|
||||
|
||||
# test original image
|
||||
idx_range = TEST_RANGE
|
||||
acc = 0.
|
||||
tot = 0.
|
||||
for idx in idx_range:
|
||||
original_image_url = original_image_base.format(idx)
|
||||
faceId = detect_face(original_image_url)
|
||||
if faceId is None:
|
||||
print("{} does not exist".format(idx))
|
||||
continue
|
||||
original_faceIds = [faceId]
|
||||
|
||||
# verify
|
||||
res = eval(original_faceIds, personGroupId, protect_personId)
|
||||
if res:
|
||||
acc += 1.
|
||||
tot += 1.
|
||||
|
||||
acc /= tot
|
||||
print(acc) # 1.0
|
||||
|
||||
|
||||
def list_personGroups():
|
||||
headers = {
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("GET", "/face/v1.0/persongroups?%s" % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = response.read()
|
||||
print(data)
|
||||
conn.close()
|
||||
|
||||
|
||||
def detect_face(image_url):
|
||||
r = requests.get(image_url)
|
||||
if r.status_code != 200:
|
||||
return None
|
||||
|
||||
headers = {
|
||||
# Request headers
|
||||
'Content-Type': 'application/json',
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
# Request parameters
|
||||
'returnFaceId': 'true',
|
||||
'returnFaceLandmarks': 'false',
|
||||
'recognitionModel': 'recognition_01',
|
||||
'returnRecognitionModel': 'false',
|
||||
'detectionModel': 'detection_01',
|
||||
})
|
||||
|
||||
body = json.dumps({
|
||||
'url': image_url
|
||||
})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("POST", "/face/v1.0/detect?%s" % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = json.loads(response.read())
|
||||
#
|
||||
# print(data)
|
||||
conn.close()
|
||||
return data[0]["faceId"]
|
||||
|
||||
|
||||
def verify_face(faceId, personGroupId, personId):
|
||||
# html header
|
||||
headers = {
|
||||
'Content-Type': 'application/json',
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
# image URL
|
||||
body = json.dumps({
|
||||
"faceId": faceId,
|
||||
"personId": personId,
|
||||
"PersonGroupId": personGroupId
|
||||
})
|
||||
|
||||
# Call Face API
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("POST", "/face/v1.0/verify?%s" % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = json.loads(response.read())
|
||||
conn.close()
|
||||
return data
|
||||
|
||||
|
||||
def create_personGroupId(personGroupId, personGroupName):
|
||||
headers = {
|
||||
# Request headers
|
||||
'Content-Type': 'application/json',
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({
|
||||
"name": personGroupName
|
||||
})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("PUT", "/face/v1.0/persongroups/{}?%s".format(personGroupId) % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = response.read()
|
||||
print(data)
|
||||
conn.close()
|
||||
|
||||
|
||||
def create_personId(personGroupId, personName):
|
||||
headers = {
|
||||
# Request headers
|
||||
'Content-Type': 'application/json',
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({
|
||||
"name": personName
|
||||
})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("POST", "/face/v1.0/persongroups/{}/persons?%s".format(personGroupId) % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = json.loads(response.read())
|
||||
# print(data)
|
||||
conn.close()
|
||||
return data["personId"]
|
||||
|
||||
|
||||
def add_persistedFaceId(personGroupId, personId, image_url):
|
||||
r = requests.get(image_url)
|
||||
if r.status_code != 200:
|
||||
return None
|
||||
|
||||
headers = {
|
||||
'Content-Type': 'application/json',
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
'personGroupId': personGroupId,
|
||||
'personId': personId
|
||||
})
|
||||
|
||||
body = json.dumps({
|
||||
'url': image_url
|
||||
})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("POST",
|
||||
"/face/v1.0/persongroups/{}/persons/{}/persistedFaces?%s".format(personGroupId, personId) % params,
|
||||
body, headers)
|
||||
response = conn.getresponse()
|
||||
data = json.loads(response.read())
|
||||
conn.close()
|
||||
if "persistedFaceId" not in data:
|
||||
return None
|
||||
return data["persistedFaceId"]
|
||||
|
||||
|
||||
def list_personGroupPerson(personGroupId):
|
||||
headers = {
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("GET", "/face/v1.0/persongroups/{}/persons?%s".format(personGroupId) % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = json.loads(response.read())
|
||||
conn.close()
|
||||
for person in data:
|
||||
print(person["personId"], len(person["persistedFaceIds"]))
|
||||
|
||||
|
||||
def get_personGroupPerson(personGroupId, personId):
|
||||
headers = {
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("GET", "/face/v1.0/persongroups/{}/persons/{}?%s".format(personGroupId, personId) % params, body,
|
||||
headers)
|
||||
response = conn.getresponse()
|
||||
data = json.loads(response.read())
|
||||
print(data)
|
||||
conn.close()
|
||||
|
||||
|
||||
def train_personGroup(personGroupId):
|
||||
headers = {
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("POST", "/face/v1.0/persongroups/{}/train?%s".format(personGroupId) % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = response.read()
|
||||
print(data)
|
||||
conn.close()
|
||||
|
||||
|
||||
def eval(original_faceIds, personGroupId, protect_personId):
|
||||
headers = {
|
||||
'Content-Type': 'application/json',
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({
|
||||
'faceIds': original_faceIds,
|
||||
'personGroupId': personGroupId,
|
||||
'maxNumOfCandidatesReturned': 1
|
||||
})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("POST", "/face/v1.0/identify?%s" % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = json.loads(response.read())
|
||||
conn.close()
|
||||
face = data[0]
|
||||
print(face)
|
||||
if len(face["candidates"]) and face["candidates"][0]["personId"] == protect_personId:
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
|
||||
def delete_personGroupPerson(personGroupId, personId):
|
||||
headers = {
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("DELETE", "/face/v1.0/persongroups/{}/persons/{}?%s".format(personGroupId, personId) % params, body,
|
||||
headers)
|
||||
response = conn.getresponse()
|
||||
data = response.read()
|
||||
print(data)
|
||||
conn.close()
|
||||
|
||||
|
||||
def get_trainStatus(personGroupId):
|
||||
headers = {
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({})
|
||||
|
||||
body = json.dumps({})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("GET", "/face/v1.0/persongroups/{}/training?%s".format(personGroupId) % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = response.read()
|
||||
conn.close()
|
||||
return data
|
||||
|
||||
|
||||
def delete_personGroup(personGroupId):
|
||||
headers = {
|
||||
'Ocp-Apim-Subscription-Key': subscription_key,
|
||||
}
|
||||
|
||||
params = urllib.parse.urlencode({
|
||||
})
|
||||
|
||||
body = json.dumps({})
|
||||
|
||||
conn = http.client.HTTPSConnection(uri_base)
|
||||
conn.request("DELETE", "/face/v1.0/persongroups/{}?%s".format(personGroupId) % params, body, headers)
|
||||
response = conn.getresponse()
|
||||
data = response.read()
|
||||
print(data)
|
||||
conn.close()
|
||||
|
||||
|
||||
def main():
|
||||
test_cloak()
|
||||
|
||||
# delete_personGroup('cloaking')
|
||||
# delete_personGroup('cloaking-emily')
|
||||
# delete_personGroup('pubfig')
|
||||
# list_personGroups()
|
||||
# exit()
|
||||
# personGroupId = 'cloaking'
|
||||
# create_personGroupId(personGroupId, 'cloaking')
|
||||
# delete_personGroupPerson(personGroupId, '0ac606cd-24b3-440f-866a-31adf2a1b446')
|
||||
# add_protect_person(personGroupId, 'Emily')
|
||||
# protect_personId = create_personId(personGroupId, 'Emily')
|
||||
# add_sybil_person(personGroupId, 'sybil')
|
||||
#
|
||||
# # train model based on personGroup
|
||||
# train_personGroup(personGroupId)
|
||||
# get_trainStatus(personGroupId)
|
||||
# add_other_person(personGroupId)
|
||||
# list_personGroupPerson(personGroupId)
|
||||
#
|
||||
# idx_range = range(72, 82)
|
||||
# original_faceIds = []
|
||||
# for idx in idx_range:
|
||||
# original_image_url = "https://super.cs.uchicago.edu/~shawn/cloaked/{}_o.png".format(idx)
|
||||
# faceId = detect_face(original_image_url)
|
||||
# original_faceIds.append(faceId)
|
||||
#
|
||||
# # verify
|
||||
# eval(original_faceIds, personGroupId, protect_personId)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_cloak()
|
||||
@ -1,41 +0,0 @@
|
||||
import glob
|
||||
import json
|
||||
import os
|
||||
|
||||
DATASETS = {
|
||||
"pubfig": "../data/pubfig",
|
||||
"scrub": "/home/shansixioing/fawkes/data/scrub/",
|
||||
"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)
|
||||
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
|
||||
with open(os.path.join(model_dir, "config.json"), "wb") as f:
|
||||
f.write(j.encode())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,75 +0,0 @@
|
||||
import argparse
|
||||
import glob
|
||||
import os
|
||||
import pickle
|
||||
import random
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
from keras.applications.vgg16 import preprocess_input
|
||||
from keras.preprocessing import image
|
||||
sys.path.append("../fawkes")
|
||||
# from utils import load_extractor
|
||||
import keras
|
||||
|
||||
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 = keras.models.load_model(args.feature_extractor)
|
||||
|
||||
path2emb = {}
|
||||
model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
|
||||
for path in glob.glob(os.path.join(model_dir, "target_data/*")):
|
||||
print(path)
|
||||
idx = int(path.split("/")[-1])
|
||||
cur_image_paths = glob.glob(os.path.join(path, "*"))
|
||||
imgs = np.array([image.img_to_array(image.load_img(p, target_size=(224, 224))) for p in cur_image_paths])
|
||||
imgs = preprocess_input(imgs)
|
||||
|
||||
cur_feature = extractor.predict(imgs)
|
||||
cur_feature = np.mean(cur_feature, axis=0)
|
||||
path2emb[idx] = cur_feature
|
||||
|
||||
model_path = os.path.join(model_dir, "{}_extract.h5".format(args.feature_extractor_name))
|
||||
emb_path = os.path.join(model_dir, "{}_emb.p".format(args.feature_extractor_name))
|
||||
extractor.save(model_path)
|
||||
pickle.dump(path2emb, open(emb_path, "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="path of the feature extractor used for optimization",
|
||||
default="/home/shansixioing/fawkes/feature_extractors/high2_extract.h5")
|
||||
parser.add_argument('--feature-extractor-name', type=str,
|
||||
help="name of the feature extractor used for optimization",
|
||||
default="high2")
|
||||
|
||||
return parser.parse_args(argv)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_arguments(sys.argv[1:])
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user