ResearchPapers/fawkes
ResearchPapers/fawkes
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1. correct a typo

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2. rewrite distance function to remove sklearn


Former-commit-id: a9e6234a80e371b559750654fc60f3b1642eb74a [formerly d2dc50e6357b516398beb374a032b7cc2f169d70]
Former-commit-id: 3e566674f17cca4b89afe4d1fecfe1009c8166b8
This commit is contained in:
smy17 2020-07-09 23:26:45 +08:00
parent feb2294993
commit 886a04ded0
3 changed files with 26 additions and 12 deletions
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14
fawkes/protection.py
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@ -102,26 +102,26 @@ class Fawkes(object):
faces = Faces(image_paths, self.sess, verbose=1)
orginal_images = faces.cropped_faces
orginal_images = np.array(orginal_images)
original_images = faces.cropped_faces
original_images = np.array(original_images)
if separate_target:
target_embedding = []
for org_img in orginal_images:
for org_img in original_images:
org_img = org_img.reshape([1] + list(org_img.shape))
tar_emb = select_target_label(org_img, self.feature_extractors_ls, self.fs_names)
target_embedding.append(tar_emb)
target_embedding = np.concatenate(target_embedding)
else:
target_embedding = select_target_label(orginal_images, self.feature_extractors_ls, self.fs_names)
target_embedding = select_target_label(original_images, self.feature_extractors_ls, self.fs_names)
protected_images = generate_cloak_images(self.sess, self.feature_extractors_ls, orginal_images,
protected_images = generate_cloak_images(self.sess, self.feature_extractors_ls, original_images,
target_emb=target_embedding, th=th, faces=faces, sd=sd,
lr=lr, max_step=max_step, batch_size=batch_size)
faces.cloaked_cropped_faces = protected_images
cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(orginal_images)
cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(original_images)
final_images = faces.merge_faces(cloak_perturbation)
for p_img, cloaked_img, path in zip(final_images, protected_images, image_paths):
@ -129,7 +129,7 @@ class Fawkes(object):
dump_image(p_img, file_name, format=format)
elapsed_time = time.time() - start_time
print('attack cost %f s' % (elapsed_time))
print('attack cost %f s' % elapsed_time)
print("Done!")

21
fawkes/utils.py
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@ -25,7 +25,6 @@ from keras.layers import Dense, Activation
from keras.models import Model
from keras.preprocessing import image
from skimage.transform import resize
from sklearn.metrics import pairwise_distances
from fawkes.align_face import align, aligner
from six.moves.urllib.request import urlopen
@ -422,11 +421,27 @@ def extractor_ls_predict(feature_extractors_ls, X):
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 calculate_dist_score(a, b, feature_extractors_ls, metric='l2'):
features1 = extractor_ls_predict(feature_extractors_ls, a)
features2 = extractor_ls_predict(feature_extractors_ls, b)
pair_cos = pairwise_distances(features1, features2, metric)
pair_cos = pairwise_l2_distance(features1, features2)
max_sum = np.min(pair_cos, axis=0)
max_sum_arg = np.argsort(max_sum)[::-1]
max_sum_arg = max_sum_arg[:len(a)]
@ -447,7 +462,7 @@ def select_target_label(imgs, feature_extractors_ls, feature_extractors_names, m
embs = [p[1] for p in items]
embs = np.array(embs)
pair_dist = pairwise_distances(original_feature_x, embs, metric)
pair_dist = pairwise_l2_distance(original_feature_x, embs)
max_sum = np.min(pair_dist, axis=0)
max_id = np.argmax(max_sum)

3
setup.py
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@ -82,8 +82,7 @@ install_requires = [
'keras==2.2.5',
'scikit-image',
'pillow>=7.0.0',
'opencv-python>=4.2.0.34',
'sklearn',
'opencv-python>=4.2.0.34'
]
setup(