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work on eval
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Shawn-Shan
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Fawkes Evaluation
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-----------------
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We offer two ways to test the protection is effective, 1) train a local face recognition model using transfer learning, 2) use Microsoft Azure API.
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Note that we can't guarantee the protection is always successful due to new development in face recognition technique.
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Evaluation with Local Model
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---------------------------
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forthcoming...
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Evaluation with Microsoft Azure
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---------------------------
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forthcoming...
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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, filter_image_paths
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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 = "_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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original_image_paths, original_loaded_images = filter_image_paths(original_image_paths)
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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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protect_image_paths, protected_loaded_images = filter_image_paths(protect_image_paths)
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print("Find {} original image and {} cloaked images".format(len(original_image_paths), len(protect_image_paths)))
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original_faces = Faces(original_image_paths, original_loaded_images, 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, protected_loaded_images, 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 or len(protect_image_paths) <= 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=1,
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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=500)
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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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