replace skimage resize with image resize

fawkes/differentiator.py

@@ -407,6 +407,8 @@ class FawkesMaskGeneration:
 
             if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0:
                 LR = LR * 0.8
+                if self.verbose:
+                    print("Learning rate: ", LR)
 
             if iteration % (self.MAX_ITERATIONS // 5) == 0:
                 if self.verbose == 1:

fawkes/protection.py

@@ -26,16 +26,6 @@ def generate_cloak_images(protector, image_X, target_emb=None):
     return cloaked_image_X
 
 
-def check_imgs(imgs):
-    if np.max(imgs) <= 1 and np.min(imgs) >= 0:
-        imgs = imgs * 255.0
-    elif np.max(imgs) <= 255 and np.min(imgs) >= 0:
-        pass
-    else:
-        raise Exception("Image values ")
-    return imgs
-
-
 class Fawkes(object):
     def __init__(self, feature_extractor, gpu, batch_size):
 
@@ -66,7 +56,7 @@ class Fawkes(object):
     def mode2param(self, mode):
         if mode == 'low':
             th = 0.003
-            max_step = 50
+            max_step = 40
             lr = 20
         elif mode == 'mid':
             th = 0.005
@@ -89,7 +79,6 @@ class Fawkes(object):
 
     def run_protection(self, image_paths, mode='low', th=0.04, sd=1e9, lr=10, max_step=500, batch_size=1, format='png',
                        separate_target=True, debug=False):
-
         if mode == 'custom':
             pass
         else:
@@ -101,11 +90,16 @@ class Fawkes(object):
         image_paths, loaded_images = filter_image_paths(image_paths)
 
         if not image_paths:
-            raise Exception("No images in the directory")
+            print("No images in the directory")
+            return 3
+
         with graph.as_default():
             faces = Faces(image_paths, loaded_images, self.aligner, verbose=1)
 
             original_images = faces.cropped_faces
+            if len(original_images) == 0:
+                print("No face detected. ")
+                return 2
             original_images = np.array(original_images)
 
             with sess.as_default():
@@ -143,16 +137,19 @@ class Fawkes(object):
 
                 faces.cloaked_cropped_faces = protected_images
 
-                cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(
+                # cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(
-                    original_images)
+                #     original_images)
-                final_images = faces.merge_faces(cloak_perturbation)
+                # final_images = faces.merge_faces(cloak_perturbation)
+
+                final_images = faces.merge_faces(reverse_process_cloaked(protected_images),
+                                                 reverse_process_cloaked(original_images))
 
         for p_img, path in zip(final_images, image_paths):
             file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), mode, format)
             dump_image(p_img, file_name, format=format)
 
         print("Done!")
-        return None
+        return 1
 
 
 def main(*argv):
@@ -201,9 +198,17 @@ def main(*argv):
     image_paths = [path for path in image_paths if "_cloaked" not in path.split("/")[-1]]
 
     protector = Fawkes(args.feature_extractor, args.gpu, args.batch_size)
-    protector.run_protection(image_paths, mode=args.mode, th=args.th, sd=args.sd, lr=args.lr, max_step=args.max_step,
+    if args.mode != 'all':
-                             batch_size=args.batch_size, format=args.format,
+        protector.run_protection(image_paths, mode=args.mode, th=args.th, sd=args.sd, lr=args.lr,
-                             separate_target=args.separate_target, debug=args.debug)
+                                 max_step=args.max_step,
+                                 batch_size=args.batch_size, format=args.format,
+                                 separate_target=args.separate_target, debug=args.debug)
+    else:
+        for m in ['low', 'mid', 'high']:
+            protector.run_protection(image_paths, mode=m, th=args.th, sd=args.sd, lr=args.lr,
+                                     max_step=args.max_step,
+                                     batch_size=args.batch_size, format=args.format,
+                                     separate_target=args.separate_target, debug=args.debug)
 
 
 if __name__ == '__main__':

fawkes/utils.py

@@ -25,7 +25,7 @@ from PIL import Image, ExifTags
 from keras.layers import Dense, Activation, Dropout
 from keras.models import Model
 from keras.preprocessing import image
-from skimage.transform import resize
+# from skimage.transform import resize
 
 from fawkes.align_face import align
 from six.moves.urllib.request import urlopen
@@ -140,7 +140,6 @@ class Faces(object):
             cur_faces_square = []
             if verbose:
                 print("Find {} face(s) in {}".format(len(cur_faces), p.split("/")[-1]))
-
             if eval_local:
                 cur_faces = cur_faces[:1]
 
@@ -152,16 +151,16 @@ class Faces(object):
                     base = np.zeros((long_size, long_size, 3))
                     base[0:img.shape[0], 0:img.shape[1], :] = img
                 cur_faces_square.append(base)
-
             cur_index = align_img[1]
             cur_faces_square = [resize(f, (224, 224)) for f in cur_faces_square]
+
             self.cropped_faces_shape.extend(cur_shapes)
             self.cropped_faces.extend(cur_faces_square)
             self.cropped_index.extend(cur_index)
             self.callback_idx.extend([i] * len(cur_faces_square))
 
-        if not self.cropped_faces:
+        if len(self.cropped_faces) == 0:
-            raise Exception("No faces detected")
+            return
 
         self.cropped_faces = np.array(self.cropped_faces)
 
@@ -173,15 +172,24 @@ class Faces(object):
     def get_faces(self):
         return self.cropped_faces
 
-    def merge_faces(self, cloaks):
+    def merge_faces(self, protected_images, original_images):
 
         self.cloaked_faces = np.copy(self.org_faces)
 
         for i in range(len(self.cropped_faces)):
-            cur_cloak = cloaks[i]
+            # cur_cloak = cloaks[i]
+            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]])
-            reshape_cloak = resize(cur_cloak, (old_square_shape, old_square_shape))
+
+            # reshape_cloak = resize(cur_cloak, (old_square_shape, old_square_shape))
+            cur_protected = resize(cur_protected, (old_square_shape, old_square_shape))
+            cur_original = resize(cur_original, (old_square_shape, old_square_shape))
+
+            reshape_cloak = cur_protected - cur_original
+
             reshape_cloak = reshape_cloak[0:org_shape[0], 0:org_shape[1], :]
 
             callback_id = self.callback_idx[i]
@@ -211,6 +219,15 @@ def load_victim_model(number_classes, teacher_model=None, end2end=False, dropout
     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_index, force=False):
     if isinstance(gpu_index, list):
         gpu_num = ','.join([str(i) for i in gpu_index])