improve performance

fawkes/__init__.py

@@ -4,7 +4,7 @@
 # @Link    : https://www.shawnshan.com/
 
 
-__version__ = '0.0.9'
+__version__ = '0.1.0'
 
 from .detect_faces import create_mtcnn, run_detect_face
 from .differentiator import FawkesMaskGeneration

fawkes/differentiator.py

@@ -48,7 +48,7 @@ class FawkesMaskGeneration:
                  max_iterations=MAX_ITERATIONS, initial_const=INITIAL_CONST,
                  intensity_range=INTENSITY_RANGE, l_threshold=L_THRESHOLD,
                  max_val=MAX_VAL, keep_final=KEEP_FINAL, maximize=MAXIMIZE, image_shape=IMAGE_SHAPE,
-                 verbose=0, ratio=RATIO, limit_dist=LIMIT_DIST, faces=None):
+                 verbose=0, ratio=RATIO, limit_dist=LIMIT_DIST):
 
         assert intensity_range in {'raw', 'imagenet', 'inception', 'mnist'}
 
@@ -70,7 +70,6 @@ class FawkesMaskGeneration:
         self.ratio = ratio
         self.limit_dist = limit_dist
         self.single_shape = list(image_shape)
-        self.faces = faces
 
         self.input_shape = tuple([self.batch_size] + self.single_shape)
 
@@ -168,7 +167,8 @@ class FawkesMaskGeneration:
         self.bottlesim_sum = 0.0
         self.bottlesim_push = 0.0
         for bottleneck_model in bottleneck_model_ls:
-            model_input_shape = bottleneck_model.input_shape[1:]
+            model_input_shape = (224, 224, 3)
+
             cur_aimg_input = resize_tensor(self.aimg_input, model_input_shape)
 
             self.bottleneck_a = bottleneck_model(cur_aimg_input)
@@ -267,7 +267,7 @@ class FawkesMaskGeneration:
               % int(np.ceil(len(source_imgs) / self.batch_size)))
 
         for idx in range(0, len(source_imgs), self.batch_size):
-            print('processing batch %d at %s' % (idx, datetime.datetime.now()))
+            print('processing image %d at %s' % (idx+1, datetime.datetime.now()))
             adv_img = self.attack_batch(source_imgs[idx:idx + self.batch_size],
                                         target_imgs[idx:idx + self.batch_size],
                                         weights[idx:idx + self.batch_size])
@@ -315,8 +315,6 @@ class FawkesMaskGeneration:
         weights_batch[:nb_imgs] = weights[:nb_imgs]
         modifier_batch = np.ones(self.input_shape) * 1e-6
 
-        temp_images = []
-
         # set the variables so that we don't have to send them over again
         if self.MIMIC_IMG:
             self.sess.run(self.setup,
@@ -407,8 +405,9 @@ class FawkesMaskGeneration:
             if all_clear:
                 break
 
-            if iteration != 0 and iteration % (self.MAX_ITERATIONS // 2) == 0:
+            if iteration != 0 and iteration % (self.MAX_ITERATIONS // 3) == 0:
                 LR = LR * 0.8
+                print("LR: {}".format(LR))
 
             if iteration % (self.MAX_ITERATIONS // 5) == 0:
                 if self.verbose == 1:

fawkes/protection.py

@@ -6,9 +6,7 @@ import argparse
 import glob
 import logging
 import os
-import random
 import sys
-import time
 
 import tensorflow as tf
 
@@ -22,26 +20,9 @@ from fawkes.utils import load_extractor, init_gpu, select_target_label, dump_ima
 from fawkes.align_face import aligner
 from fawkes.utils import get_file
 
-random.seed(12243)
-np.random.seed(122412)
 
-
+def generate_cloak_images(protector, image_X, target_emb=None):
-def generate_cloak_images(sess, feature_extractors, image_X, target_emb=None, th=0.01, faces=None, sd=1e9, lr=2,
+    cloaked_image_X = protector.attack(image_X, target_emb)
-                          max_step=500, batch_size=1, debug=False):
-    batch_size = batch_size if len(image_X) > batch_size else len(image_X)
-
-    differentiator = FawkesMaskGeneration(sess, feature_extractors,
-                                          batch_size=batch_size,
-                                          mimic_img=True,
-                                          intensity_range='imagenet',
-                                          initial_const=sd,
-                                          learning_rate=lr,
-                                          max_iterations=max_step,
-                                          l_threshold=th,
-                                          verbose=1 if debug else 0, maximize=False, keep_final=False, image_shape=image_X.shape[1:],
-                                          faces=faces)
-
-    cloaked_image_X = differentiator.attack(image_X, target_emb)
     return cloaked_image_X
 
 
@@ -79,6 +60,9 @@ class Fawkes(object):
         self.aligner = aligner(sess)
         self.feature_extractors_ls = [load_extractor(name) for name in self.fs_names]
 
+        self.protector = None
+        self.protector_param = None
+
     def mode2param(self, mode):
         if mode == 'low':
             th = 0.003
@@ -86,12 +70,12 @@ class Fawkes(object):
             lr = 20
         elif mode == 'mid':
             th = 0.005
-            max_step = 100
+            max_step = 120
-            lr = 20
+            lr = 15
         elif mode == 'high':
             th = 0.008
-            max_step = 200
+            max_step = 600
-            lr = 20
+            lr = 10
         elif mode == 'ultra':
             if not tf.test.is_gpu_available():
                 print("Please enable GPU for ultra setting...")
@@ -103,7 +87,7 @@ class Fawkes(object):
             raise Exception("mode must be one of 'low', 'mid', 'high', 'ultra', 'custom'")
         return th, max_step, lr
 
-    def run_protection(self, image_paths, mode='mid', th=0.04, sd=1e9, lr=10, max_step=500, batch_size=1, format='png',
+    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':
@@ -111,6 +95,9 @@ class Fawkes(object):
         else:
             th, max_step, lr = self.mode2param(mode)
 
+        current_param = "-".join([str(x) for x in [mode, th, sd, lr, max_step, batch_size, format,
+                                                   separate_target, debug]])
+
         image_paths, loaded_images = filter_image_paths(image_paths)
 
         if not image_paths:
@@ -132,9 +119,27 @@ class Fawkes(object):
                 else:
                     target_embedding = select_target_label(original_images, self.feature_extractors_ls, self.fs_names)
 
-                protected_images = generate_cloak_images(sess, self.feature_extractors_ls, original_images,
+                if current_param != self.protector_param:
-                                                         target_emb=target_embedding, th=th, faces=faces, sd=sd,
+                    self.protector_param = current_param
-                                                         lr=lr, max_step=max_step, batch_size=batch_size, debug=debug)
+
+                    if self.protector is not None:
+                        del self.protector
+
+                    self.protector = FawkesMaskGeneration(sess, self.feature_extractors_ls,
+                                                          batch_size=batch_size,
+                                                          mimic_img=True,
+                                                          intensity_range='imagenet',
+                                                          initial_const=sd,
+                                                          learning_rate=lr,
+                                                          max_iterations=max_step,
+                                                          l_threshold=th,
+                                                          verbose=1 if debug else 0,
+                                                          maximize=False,
+                                                          keep_final=False,
+                                                          image_shape=(224, 224, 3))
+
+                protected_images = generate_cloak_images(self.protector, original_images,
+                                                         target_emb=target_embedding)
 
                 faces.cloaked_cropped_faces = protected_images
 
@@ -145,7 +150,7 @@ class Fawkes(object):
         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)
-        # elapsed_time = time.time() - start_time
+
         print("Done!")
         return None
 

fawkes/utils.py

@@ -141,6 +141,9 @@ class Faces(object):
             if verbose:
                 print("Find {} face(s) in {}".format(len(cur_faces), p.split("/")[-1]))
 
+            if eval_local:
+                cur_faces = cur_faces[:1]
+
             for img in cur_faces:
                 if eval_local:
                     base = resize(img, (224, 224))
@@ -150,6 +153,7 @@ class Faces(object):
                     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)

setup.py

@@ -77,7 +77,7 @@ setup_requires = []
 install_requires = [
     'numpy==1.16.4',
     # 'tensorflow-gpu>=1.13.1, <=1.14.0',
-    'tensorflow>=1.12.0, <=1.15.0',
+    'tensorflow>=1.12.0, <=1.15.0', # change this is tensorflow-gpu if using GPU machine. 
     'argparse',
     'keras>=2.2.5, <=2.3.1',
     'scikit-image',
@@ -88,7 +88,7 @@ install_requires = [
 setup(
     name='fawkes',
     version=__version__,
-    license='MIT',
+    license='BSD',
     description='An utility to protect user privacy',
     long_description=long_description,
     long_description_content_type='text/markdown',
@@ -114,5 +114,5 @@ setup(
     },
     include_package_data=True,
     zip_safe=False,
-    python_requires='>=3.5',
+    python_requires='>=3.5,<3.8',
 )