add app

Former-commit-id: e7e46967035dfb727d180de0a0780ca9e026dd02 [formerly 0e703ac63e52aafbaa3033759553e2f3b31d2886]
Former-commit-id: 9dfff8ea4c2646d90203b378e0732330e655086a

app/app.py

@@ -0,0 +1,63 @@
+import threading
+
+import fawkes.protection
+
+from tkinter import Tk, BOTH, StringVar
+from tkinter.ttk import Frame, Label, Style, Button
+from tkinter.filedialog import askdirectory, askopenfilenames
+
+
+class UI(Frame):
+    def __init__(self):
+        super().__init__()
+        self.my_fawkes = fawkes.protection.Fawkes("high_extract", '0', 1)
+        self.var = StringVar()
+        self.var.set('Initial')
+        self.img_paths = './imgs'
+        self.initUI()
+
+    def initUI(self):
+        self.master.title("This is a Window")
+        self.pack(fill=BOTH, expand=1)
+
+        btn_Open = Button(self,
+                          text='open img directory',
+                          width=30,
+                          command=self.select_path)
+        btn_Open.pack()
+
+        btn_Run = Button(self,
+                         text='run the code',
+                         width=3,
+                         command=lambda: thread_it(self.my_fawkes.run_protection, self.img_paths))
+        btn_Run.pack()
+
+        Label_Show = Label(self,
+                           textvariable=self.var,
+                           font=('Arial', 13), width=50)
+        Label_Show.pack()
+
+    def select_path(self):
+        self.img_paths = askopenfilenames(filetypes=[('image', "*.gif *.jpg *.png")])
+        self.var.set('the paths have been set')
+
+
+root = Tk()
+root.title('window')
+root.geometry('600x500')
+app = UI()
+
+
+def main():
+    root.mainloop()
+
+
+def thread_it(func, *args):
+    app.var.set('cloak in process')
+    t = threading.Thread(target=func, args=args)
+    t.setDaemon(True)
+    t.start()
+
+
+if __name__ == '__main__':
+    main()

fawkes/__init__.py

@@ -8,7 +8,7 @@ __version__ = '0.0.6'
 
 from .detect_faces import create_mtcnn, run_detect_face
 from .differentiator import FawkesMaskGeneration
-from .protection import main
+from .protection import main, Fawkes
 from .utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, Faces, get_file
 
 __all__ = (
@@ -16,5 +16,5 @@ __all__ = (
     'FawkesMaskGeneration', 'load_extractor',
     'init_gpu',
     'select_target_label', 'dump_image', 'reverse_process_cloaked',
-    'Faces', 'get_file', 'main',
+    'Faces', 'get_file', 'main', 'Fawkes'
 )

fawkes/differentiator.py

@@ -405,9 +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 // 2) == 0:
-                    # LR = LR / 2
+                    LR = LR * 0.8
-                    # print("Learning Rate: ", LR)
+                    print("Learning Rate: ", LR)
 
                 if iteration % (self.MAX_ITERATIONS // 5) == 0:
                     if self.verbose == 1:

fawkes/protection.py

@@ -18,7 +18,7 @@ import numpy as np
 from fawkes.differentiator import FawkesMaskGeneration
 from fawkes.utils import load_extractor, init_gpu, select_target_label, dump_image, reverse_process_cloaked, \
     Faces
-
+from fawkes.align_face import aligner
 random.seed(12243)
 np.random.seed(122412)
 
@@ -54,10 +54,14 @@ def check_imgs(imgs):
 
 class Fawkes(object):
     def __init__(self, feature_extractor, gpu, batch_size):
+        global graph
+        graph = tf.get_default_graph()
+
         self.feature_extractor = feature_extractor
         self.gpu = gpu
         self.batch_size = batch_size
         self.sess = init_gpu(gpu)
+        self.aligner = aligner(self.sess)
         self.fs_names = [feature_extractor]
         if isinstance(feature_extractor, list):
             self.fs_names = feature_extractor
@@ -67,23 +71,23 @@ class Fawkes(object):
     def mode2param(self, mode):
         if mode == 'low':
             th = 0.003
-            max_step = 20
+            max_step = 50
             lr = 20
         elif mode == 'mid':
             th = 0.005
-            max_step = 50
+            max_step = 100
-            lr = 15
+            lr = 30
         elif mode == 'high':
             th = 0.008
-            max_step = 500
+            max_step = 200
-            lr = 15
+            lr = 20
         elif mode == 'ultra':
             if not tf.test.is_gpu_available():
                 print("Please enable GPU for ultra setting...")
                 sys.exit(1)
             th = 0.01
-            max_step = 2000
+            max_step = 200
-            lr = 8
+            lr = 20
         else:
             raise Exception("mode must be one of 'low', 'mid', 'high', 'ultra', 'custom'")
         return th, max_step, lr
@@ -99,38 +103,38 @@ class Fawkes(object):
 
         if not image_paths:
             raise Exception("No images in the directory")
+        with graph.as_default():
+            faces = Faces(image_paths, self.aligner, verbose=1)
 
-        faces = Faces(image_paths, self.sess, verbose=1)
+            original_images = faces.cropped_faces
+            original_images = np.array(original_images)
 
-        original_images = faces.cropped_faces
+            if separate_target:
-        original_images = np.array(original_images)
+                target_embedding = []
+                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(original_images, self.feature_extractors_ls, self.fs_names)
 
-        if separate_target:
+            protected_images = generate_cloak_images(self.sess, self.feature_extractors_ls, original_images,
-            target_embedding = []
+                                                     target_emb=target_embedding, th=th, faces=faces, sd=sd,
-            for org_img in original_images:
+                                                     lr=lr, max_step=max_step, batch_size=batch_size)
-                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(original_images, self.feature_extractors_ls, self.fs_names)
 
-        protected_images = generate_cloak_images(self.sess, self.feature_extractors_ls, original_images,
+            faces.cloaked_cropped_faces = protected_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(original_images)
+            final_images = faces.merge_faces(cloak_perturbation)
 
-        cloak_perturbation = reverse_process_cloaked(protected_images) - reverse_process_cloaked(original_images)
+            for p_img, cloaked_img, path in zip(final_images, protected_images, image_paths):
-        final_images = faces.merge_faces(cloak_perturbation)
+                file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), mode, format)
+                dump_image(p_img, file_name, format=format)
 
-        for p_img, cloaked_img, path in zip(final_images, protected_images, image_paths):
+            elapsed_time = time.time() - start_time
-            file_name = "{}_{}_cloaked.{}".format(".".join(path.split(".")[:-1]), mode, format)
+            print('attack cost %f s' % elapsed_time)
-            dump_image(p_img, file_name, format=format)
+            print("Done!")
-
-        elapsed_time = time.time() - start_time
-        print('attack cost %f s' % elapsed_time)
-        print("Done!")
 
 
 def main(*argv):

fawkes/utils.py

@@ -86,7 +86,7 @@ def load_image(path):
 
 
 class Faces(object):
-    def __init__(self, image_paths, sess, verbose=1):
+    def __init__(self, image_paths, aligner, verbose=1):
         model_dir = os.path.join(os.path.expanduser('~'), '.fawkes')
         if not os.path.exists(os.path.join(model_dir, "mtcnn.p.gz")):
             os.makedirs(model_dir, exist_ok=True)
@@ -94,7 +94,7 @@ class Faces(object):
                      cache_subdir='')
 
         self.verbose = verbose
-        self.aligner = aligner(sess)
+        self.aligner = aligner
         self.org_faces = []
         self.cropped_faces = []
         self.cropped_faces_shape = []

fawkes_dev/azure_add_images.py

@@ -14,10 +14,102 @@ data = f.read().split("\n")
 subscription_key = data[0]
 uri_base = data[1]
 
-cloak_image_base = 'http://sandlab.cs.uchicago.edu/fawkes/files/cloak/{}_ultra_cloaked.png'
+cloak_image_base = 'http://sandlab.cs.uchicago.edu/fawkes/files/cloak/{}_high_cloaked.png'
 original_image_base = 'http://sandlab.cs.uchicago.edu/fawkes/files/cloak/{}.png'
 
 
+def test_cloak():
+    NUM_TRAIN = 5
+    total_idx = range(0, 82)
+    TRAIN_RANGE = random.sample(total_idx, NUM_TRAIN)
+
+    TEST_RANGE = random.sample([i for i in total_idx if i not in TRAIN_RANGE], 20)
+
+    personGroupId = 'all'
+
+    # delete_personGroup(personGroupId)
+    # create_personGroupId(personGroupId, personGroupId)
+
+    with open("protect_personId.txt", 'r') as f:
+        protect_personId = f.read()
+    print(protect_personId)
+    delete_personGroupPerson(personGroupId, protect_personId)
+
+    protect_personId = create_personId(personGroupId, 'Emily')
+    with open("protect_personId.txt", 'w') as f:
+        f.write(protect_personId)
+
+    print("Created protect personId: {}".format(protect_personId))
+    for idx in TRAIN_RANGE:
+        image_url = cloak_image_base.format(idx)
+        r = add_persistedFaceId(personGroupId, protect_personId, image_url)
+        if r is not None:
+            print("Added {}".format(idx))
+        else:
+            print("Unable to add {}-th image of protect person".format(idx))
+
+    # add other people
+    # 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:
@@ -274,98 +366,6 @@ def get_trainStatus(personGroupId):
     return data
 
 
-def test_cloak():
-    NUM_TRAIN = 10
-    total_idx = range(0, 82)
-    TRAIN_RANGE = random.sample(total_idx, NUM_TRAIN)
-
-    TEST_RANGE = random.sample([i for i in total_idx if i not in TRAIN_RANGE], 20)
-
-    personGroupId = 'all'
-
-    # delete_personGroup(personGroupId)
-    # create_personGroupId(personGroupId, personGroupId)
-
-    with open("protect_personId.txt", 'r') as f:
-        protect_personId = f.read()
-    print(protect_personId)
-    delete_personGroupPerson(personGroupId, protect_personId)
-
-    protect_personId = create_personId(personGroupId, 'Emily')
-    with open("protect_personId.txt", 'w') as f:
-        f.write(protect_personId)
-
-    print("Created protect personId: {}".format(protect_personId))
-    for idx in TRAIN_RANGE:
-        image_url = cloak_image_base.format(idx)
-        r = add_persistedFaceId(personGroupId, protect_personId, image_url)
-        if r is not None:
-            print("Added {}".format(idx))
-        else:
-            print("Unable to add {}-th image of protect person".format(idx))
-
-    # add other people
-    # for idx_person in range(1300, 5000):
-    #     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 delete_personGroup(personGroupId):
     headers = {
         'Ocp-Apim-Subscription-Key': subscription_key,

setup.py

@@ -82,7 +82,6 @@ install_requires = [
     'keras==2.2.5',
     'scikit-image',
     'pillow>=7.0.0',
-    'opencv-python>=4.2.0.34'
 ]
 
 setup(