“update”

examples/community/face_adversarial_attack/README.md

@@ -3,12 +3,12 @@
 
 
 ##描述
-本项目是对人脸识别模型的物理对抗攻击，通过生成对抗口罩，使人脸佩戴后实现目标攻击和非目标攻击，并应用于mindspore平台。
+本项目是对人脸识别模型的物理对抗攻击，通过生成对抗口罩，使人脸佩戴后实现有目标攻击和非目标攻击，并应用于MindSpore平台。
 
 
 
 ##模型结构
-采用华为mindspore官方训练的FaceRecognition模型
+采用华为MindSpore官方训练的FaceRecognition模型
 https://www.mindspore.cn/resources/hub/details?MindSpore/1.7/facerecognition_ms1mv2
 
 
@@ -21,16 +21,15 @@ mindspore=1.7，硬件平台为GPU。
 
 ##脚本说明
 ├── readme.md
-├── opencv_photo
+├── photos
 │   ├── adv_input //对抗图像
 │   ├── input //输入图像 
 │   └── target //目标图像
 ├── outputs //训练后的图像
 ├── FaceRecognition //模型设置
-├── AFR.py  //训练脚本
-├── camera.py //opencv图像采集
+├── adversarial_attack.py  //训练脚本
 │── example_non-target_attack.py  //无目标攻击训练
-│── example_target_attack.py  //目标攻击训练
+│── example_target_attack.py  //有目标攻击训练
 │── loss_design.py  //训练优化设置
 └── test.py  //评估攻击效果
 
@@ -58,14 +57,14 @@ mindspore=1.7，硬件平台为GPU。
     利用 MindSpore代码仓中的https://gitee.com/mindspore/models/blob/master/research/cv/FaceRecognition/eval.py的get_model函数加载模型
 
 ##训练过程
-目标攻击:
+有目标攻击:
     
-    $ cd face_adversarial_attack/example/
+    $ cd face_adversarial_attack/
     $ python example_target_attack.py
 
 非目标攻击:
     
-    $ cd face_adversarial_attack/example/
+    $ cd face_adversarial_attack/
     $ python example_non-target_attack.py
 
 
@@ -78,9 +77,13 @@ optimizer=adam, learning rate=0.01, weight_decay=0.0001, epoch=2000
 ##评估过程
 评估方法一:
    
-    AFR.Attack.test()
+    adversarial_attack.Attack.test()
+
 
 评估方法二:
     
-    $ cd face_adversarial_attack/example/
+    $ cd face_adversarial_attack/
     $ python test.py
+
+
+

examples/community/face_adversarial_attack/AFR.py → examples/community/face_adversarial_attack/adversarial_attack.py

@@ -15,39 +15,38 @@
 import os
 import re
 import numpy as np
+import face_recognition as fr
+import face_recognition_models as frm
+import dlib
+import matplotlib.image as mp
+from PIL import Image, ImageDraw
 import mindspore
 import mindspore.dataset.vision.py_transforms as P
 from mindspore.dataset.vision.py_transforms import ToPIL as ToPILImage
-from mindspore import Parameter, ops, nn, Tensor
 from mindspore.dataset.vision.py_transforms import ToTensor
-import dlib
-import matplotlib.image as mp
-import face_recognition as fr
-import face_recognition_models as frm
-from PIL import Image, ImageDraw
+from mindspore import Parameter, ops, nn, Tensor
 from loss_design import MyTrainOneStepCell, MyWithLossCell, FaceLossTargetAttack, FaceLossNoTargetAttack
 from FaceRecognition.eval import get_net
 
 class FaceAdversarialAttack(object):
     """
-    Class used to create adversarial facial recognition attacks
+    Class used to create adversarial facial recognition attacks.
     """
 
     def __init__(self, input_img, target_img, seed=None):
         """
-        Initialization for Attack class.
+        Initialization for attack class.
 
         Args:
-            input_img : Image to train on.
-            target_img : Image to target the adversarial attack against.
-            seed : optional Sets custom seed for reproducability. Default is generated randomly.
+            input_img (numpy.ndarray): The input image.
+            target_img (numpy.ndarray): The target image.
+            seed (int): optional Sets custom seed for reproducability. Default is generated randomly.
 
         """
 
         if (seed is not None): np.random.seed(seed)
         self.MEAN = Tensor([0.485, 0.456, 0.406])
         self.STD = Tensor([0.229, 0.224, 0.225])
-        self.LOSS = Tensor(0)
         self.expand_dims = mindspore.ops.ExpandDims()
         self.imageize = ToPILImage()
         self.tensorize = ToTensor()
@@ -55,8 +54,8 @@ class FaceAdversarialAttack(object):
         self.resnet = get_net()
         self.input_tensor = Tensor(self.normalize(self.tensorize(input_img)))
         self.target_tensor = Tensor(self.normalize(self.tensorize(target_img)))
-        mp.imsave('./outputs/input图像.jpg', np.transpose(self._reverse_norm(self.input_tensor).asnumpy(), (1, 2, 0)))
-        mp.imsave('./outputs/target图像.jpg',
+        mp.imsave('./outputs/input_image.jpg', np.transpose(self._reverse_norm(self.input_tensor).asnumpy(), (1, 2, 0)))
+        mp.imsave('./outputs/target_image.jpg',
                   np.transpose(self._reverse_norm(self.target_tensor).asnumpy(), (1, 2, 0)))
 
 
@@ -73,14 +72,22 @@ class FaceAdversarialAttack(object):
     def train(self, attack_method):
         """
         Optimized adversarial image.
+
+        Args:
+            attack_method (Sting) : Including target attack and non-target attack.
+
+      Returns:
+            Tensor, adversarial image.
+            Tensor, mask image.
+
         """
 
         if attack_method == "non-target attack":
-            LOSS = FaceLossNoTargetAttack(self.target_emb)
+            loss = FaceLossNoTargetAttack(self.target_emb)
         if attack_method == "target_attack":
-            LOSS = FaceLossTargetAttack(self.target_emb)
+            loss = FaceLossTargetAttack(self.target_emb)
 
-        net_with_criterion = MyWithLossCell(self.resnet, LOSS, self.input_tensor)
+        net_with_criterion = MyWithLossCell(self.resnet, loss, self.input_tensor)
         train_net = MyTrainOneStepCell(net_with_criterion, self.opt)
 
         for i in range(2000):
@@ -136,13 +143,13 @@ class FaceAdversarialAttack(object):
 
     def _reverse_norm(self, image_tensor):
         """
-        Reverses normalization for a given image_tensor
+        Reverses normalization for a given image_tensor.
 
         Args:
-            image_tensor : Tensor
+            image_tensor (Tensor): image.
 
         Returns:
-            Tensor
+            Tensor, image.
         """
         tensor = image_tensor * self.STD[:, None, None] + self.MEAN[:, None, None]
 
@@ -157,12 +164,12 @@ class FaceAdversarialAttack(object):
         Apply a mask over an image.
 
         Args:
-            image_tensor : Canvas to be used to apply mask on.
-            mask_tensor : Mask to apply over the image.
-            reference_tensor : Used to reference mask boundaries
+            image_tensor (Tensor): Canvas to be used to apply mask on.
+            mask_tensor (Tensor): Mask to apply over the image.
+            reference_tensor (Tensor): Used to reference mask boundaries
 
         Returns:
-            Tensor
+            Tensor, image.
         """
         tensor = mindspore.numpy.where((reference_tensor == 0), image_tensor, mask_tensor)
 
@@ -173,10 +180,10 @@ class FaceAdversarialAttack(object):
         Create mask image.
 
         Args:
-            face_image : image of a detected face.
+            face_image (PIL.Image): image of a detected face.
 
         Returns:
-            mask_tensor : A mask image.
+            mask_tensor : a mask image.
         """
 
         mask = Image.new('RGB', face_image.size, color=(0, 0, 0))
@@ -211,10 +218,10 @@ class FaceAdversarialAttack(object):
         Reverses normalization for a given image_tensor.
 
         Args:
-            image_tensor : Tensor.
+            image_tensor (Tensor): Tensor.
 
         Returns:
-            Tensor.
+            Tensor, image.
         """
         tensor = image_tensor * self.STD[:, None, None] + self.MEAN[:, None, None]
         return tensor
@@ -227,11 +234,10 @@ def detect_face(image_loc):
     detector.
 
      Args:
-        image_loc : image file location.
+        image_loc (numpy.ndarray): image file location.
 
      Returns:
         face_image : Resized face image.
-
      """
 
     detector = dlib.get_frontal_face_detector()
@@ -253,7 +259,7 @@ def load_data(path_to_data):
     and test images.
 
     Args:
-        path_to_data : Path to the given data.
+        path_to_data (String): Path to the given data.
 
     Returns:
         list : List of resized face images.

examples/community/face_adversarial_attack/camera.py

@@ -1,34 +0,0 @@
-# Copyright 2022 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-import cv2
-
-
-capture = cv2.VideoCapture(0)
-
-
-for i in range(2):
-    while(True):
-        ret, frame = capture.read()
-        width, height = capture.get(3), capture.get(4)
-        cv2.imwrite('./opencv_photo/input/input'+str(i)+'.png', frame)
-        cv2.imshow('frame', frame)
-        if cv2.waitKey(1) == ord('q'):
-            break
-
-
-print(width, height)
-
-capture.release()
-cv2.destroyAllWindows()

examples/community/face_adversarial_attack/example_non-target_attack.py

@@ -16,7 +16,7 @@
 import numpy as np
 import matplotlib.image as mp
 from mindspore import context
-import AFR
+import ad
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
@@ -25,8 +25,8 @@ context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
 if __name__ == '__main__':
 
-    inputs = AFR.load_data('opencv_photo/input/')
-    targets = AFR.load_data('opencv_photo/target/')
+    inputs = AFR.load_data('photos/input/')
+    targets = AFR.load_data('photos/target/')
 
 
     adversarial = AFR.FaceAdversarialAttack(inputs[0], targets[0])
@@ -34,7 +34,7 @@ if __name__ == '__main__':
 
     adversarial_tensor, mask_tensor  = adversarial.train(attack_method)
 
-    mp.imsave('./outputs/对抗图像.jpg', np.transpose(adversarial_tensor.asnumpy(), (1, 2, 0)))
-    mp.imsave('./outputs/口罩.jpg', np.transpose(mask_tensor.asnumpy(), (1, 2, 0)))
+    mp.imsave('./outputs/adversarial_example.jpg', np.transpose(adversarial_tensor.asnumpy(), (1, 2, 0)))
+    mp.imsave('./outputs/mask.jpg', np.transpose(mask_tensor.asnumpy(), (1, 2, 0)))
 
     adversarial.test()

examples/community/face_adversarial_attack/example_target_attack.py

@@ -25,8 +25,8 @@ context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
 if __name__ == '__main__':
 
-    inputs = AFR.load_data('opencv_photo/input/')
-    targets = AFR.load_data('opencv_photo/target/')
+    inputs = AFR.load_data('photos/input/')
+    targets = AFR.load_data('photos/target/')
 
 
     adversarial = AFR.FaceAdversarialAttack(inputs[0], targets[0])
@@ -35,7 +35,7 @@ if __name__ == '__main__':
 
     adversarial_tensor, mask_tensor  = adversarial.train(attack_method)
 
-    mp.imsave('./outputs/对抗图像.jpg', np.transpose(adversarial_tensor.asnumpy(), (1, 2, 0)))
-    mp.imsave('./outputs/口罩.jpg', np.transpose(mask_tensor.asnumpy(), (1, 2, 0)))
+    mp.imsave('./outputs/adversarial_example.jpg', np.transpose(adversarial_tensor.asnumpy(), (1, 2, 0)))
+    mp.imsave('./outputs/mask.jpg', np.transpose(mask_tensor.asnumpy(), (1, 2, 0)))
 
     adversarial.test()

examples/community/face_adversarial_attack/loss_design.py

@@ -105,8 +105,9 @@ class FaceLossTargetAttack(nn.Cell):
 
 class FaceLossNoTargetAttack(nn.Cell):
     """The loss function of the non-target attack"""
+    
     def __init__(self, target_emb):
-        """初始化"""
+        """Initialization"""
         super(FaceLossNoTargetAttack, self).__init__()
         self.uniformreal = ops.UniformReal(seed=2)
         self.sum = ops.ReduceSum(keep_dims=False)

examples/community/face_adversarial_attack/test.py

@@ -33,9 +33,9 @@ if __name__ == '__main__':
     The input image, target image and adversarial image are tested using the FaceRecognition model.
     """
 
-    image = AFR.load_data('opencv_photo/adv_input')
-    inputs = AFR.load_data('opencv_photo/input/')
-    targets = AFR.load_data('opencv_photo/target/')
+    image = AFR.load_data('photos/adv_input/')
+    inputs = AFR.load_data('photos/input/')
+    targets = AFR.load_data('photos/target/')
 
     tensorize = ToTensor()
     normalize = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
@@ -45,7 +45,7 @@ if __name__ == '__main__':
 
     resnet = get_net()
 
-    image = mp.imread("./对抗图像.jpg")
+    image = mp.imread("./outputs/adversarial_example.jpg")
     adv = Tensor(normalize(tensorize(image)))
 
     input_tensor = Tensor(normalize(tensorize(inputs[0])))