From e31359b621e40018d670790378109da03f0f960a Mon Sep 17 00:00:00 2001 From: lmj Date: Mon, 19 Sep 2022 19:35:18 +0800 Subject: [PATCH] update --- .../face_adversarial_attack/example/AFR.py | 37 ++++++++++--------- .../example/loss_design.py | 29 ++++++++------- .../face_adversarial_attack/example/test.py | 2 +- 3 files changed, 37 insertions(+), 31 deletions(-) diff --git a/examples/face_adversarial_attack/example/AFR.py b/examples/face_adversarial_attack/example/AFR.py index 3b64290..3a82fad 100644 --- a/examples/face_adversarial_attack/example/AFR.py +++ b/examples/face_adversarial_attack/example/AFR.py @@ -22,10 +22,10 @@ import face_recognition_models as frm from PIL import Image, ImageDraw import mindspore from mindspore.dataset.vision.py_transforms import ToPIL as ToPILImage -from mindspore import Parameter,ops, nn,Tensor +from mindspore import Parameter, ops, nn, Tensor from mindspore.dataset.vision.py_transforms import ToTensor import mindspore.dataset.vision.py_transforms as P -from loss_design import TrainOneStepCell,MyWithLossCell,FaceLoss_no_target_attack,FaceLoss_target_attack +from loss_design import TrainOneStepCell,MyWithLossCell, FaceLossTargeTattack,FaceLossNoTargetAttack from FaceRecognition.eval import get_net class Attack(object): @@ -33,7 +33,7 @@ class Attack(object): Class used to create adversarial facial recognition attacks """ - def __init__(self,input_img,target_img,seed=None): + def __init__(self, input_img, target_img, seed=None): """ Initialization for Attack class. @@ -44,7 +44,7 @@ class Attack(object): """ - if (seed != None): np.random.seed(seed) + 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) @@ -56,11 +56,12 @@ class Attack(object): 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', np.transpose(self._reverse_norm(self.target_tensor).asnumpy(), (1, 2, 0))) + mp.imsave('./outputs/target图像.jpg', + np.transpose(self._reverse_norm(self.target_tensor).asnumpy(), (1, 2, 0))) - self.input_emb = self.resnet(self.expand_dims(self.input_tensor,0)) - self.target_emb = self.resnet(self.expand_dims(self.target_tensor,0)) + self.input_emb = self.resnet(self.expand_dims(self.input_tensor, 0)) + self.target_emb = self.resnet(self.expand_dims(self.target_tensor, 0)) self.adversarial_emb = None self.mask_tensor = self._create_mask(input_img) self.ref = self.mask_tensor @@ -69,28 +70,29 @@ class Attack(object): - def train(self,attack_method): + def train(self, attack_method): """ Optimized adversarial image. """ if attack_method == "non-target attack": - LOSS = FaceLoss_no_target_attack(self.target_emb) + LOSS = FaceLossNoTargetAttack(self.target_emb) if attack_method == "target_attack": - LOSS = FaceLoss_target_attack(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 = TrainOneStepCell(net_with_criterion, self.opt) for i in range(2000): self.mask_tensor = Tensor(self.pm) - grads,loss = train_net(self.mask_tensor) + grads, loss = train_net(self.mask_tensor) print("epoch %d ,loss: %f \n " % (i, loss.asnumpy().item())) - self.mask_tensor = ops.clip_by_value(self.mask_tensor, Tensor(0, mindspore.float32), Tensor(1, mindspore.float32)) + self.mask_tensor = ops.clip_by_value( + self.mask_tensor, Tensor(0, mindspore.float32), Tensor(1, mindspore.float32)) adversarial_tensor = self._apply( self.input_tensor, @@ -124,15 +126,16 @@ class Attack(object): print("================================") print("adversarial:", adversarial) print("adversarial_confidence:", self.adversarial_emb.asnumpy()[0][adversarial]) - print("Confidence changes for target::", self.adversarial_emb.asnumpy()[0][target]) + print("Confidence changes for target:", self.adversarial_emb.asnumpy()[0][target]) print("Confidence changes for input:", self.adversarial_emb.asnumpy()[0][input]) print("================================") print("target:", target) print("target_confidence:", self.target_emb.asnumpy()[0][target]) - print("input:%d, target:%d, adversarial:%d" % (input,target, adversarial)) + print("input: %d, target: %d, adversarial: %d" % (input, target, adversarial)) - def test1(self,adversarial_tensor): - self.adversarial_emb = self.resnet(self.expand_dims((adversarial_tensor - self.MEAN[:, None, None]) / self.STD[:, None, None], 0)) + def test1(self, adversarial_tensor): + self.adversarial_emb = self.resnet( + self.expand_dims((adversarial_tensor - self.MEAN[:, None, None]) / self.STD[:, None, None], 0)) self.input_emb = self.resnet(self.expand_dims(self.input_tensor, 0)) self.target_emb = self.resnet(self.expand_dims(self.target_tensor, 0)) diff --git a/examples/face_adversarial_attack/example/loss_design.py b/examples/face_adversarial_attack/example/loss_design.py index df8015c..61d718a 100644 --- a/examples/face_adversarial_attack/example/loss_design.py +++ b/examples/face_adversarial_attack/example/loss_design.py @@ -13,7 +13,7 @@ # limitations under the License. # ============================================================================ import mindspore -from mindspore import ops, nn,Tensor +from mindspore import ops, nn, Tensor from mindspore.dataset.vision.py_transforms import ToTensor import mindspore.dataset.vision.py_transforms as P @@ -41,17 +41,17 @@ class TrainOneStepCell(nn.Cell): self.weights = self.optimizer.parameters self.grad = ops.composite.GradOperation(get_all=True, sens_param=False) - def construct(self,*inputs): + def construct(self, *inputs): """Defines the computation performed.""" loss = self.network(*inputs) grads = self.grad(self.network)(*inputs) self.optimizer(grads) - return grads,loss + return grads, loss class MyWithLossCell(nn.Cell): - def __init__(self,net,loss_fn,input_tensor): + def __init__(self, net, loss_fn, input_tensor): super(MyWithLossCell, self).__init__(auto_prefix=False) self.net = net self._loss_fn = loss_fn @@ -63,11 +63,14 @@ class MyWithLossCell(nn.Cell): self.input_tensor = input_tensor self.input_emb = self.net(self.expand_dims(self.input_tensor, 0)) - def construct(self,mask_tensor): + def construct(self, mask_tensor): ref = mask_tensor - adversarial_tensor = mindspore.numpy.where((ref == 0), self.input_tensor, (mask_tensor - self.MEAN[:, None, None] )/ self.STD[:, None, None]) + adversarial_tensor = mindspore.numpy.where( + (ref == 0), + self.input_tensor, + (mask_tensor - self.MEAN[:, None, None] )/ self.STD[:, None, None]) adversarial_emb = self.net(self.expand_dims(adversarial_tensor, 0)) - loss = self._loss_fn( adversarial_emb,self.input_emb,mask_tensor) + loss = self._loss_fn( adversarial_emb, self.input_emb, mask_tensor) return loss @property @@ -75,11 +78,11 @@ class MyWithLossCell(nn.Cell): return self.net -class FaceLoss_target_attack(nn.Cell): +class FaceLossTargeTattack(nn.Cell): """The loss function of the target attack""" - def __init__(self,target_emb): - super(FaceLoss_target_attack, self).__init__() + def __init__(self, target_emb): + super(FaceLossTargeTattack, self).__init__() self.uniformreal = ops.UniformReal(seed=2) self.sum = ops.ReduceSum(keep_dims=False) self.norm = nn.Norm(keep_dims=True) @@ -92,7 +95,7 @@ class FaceLoss_target_attack(nn.Cell): self.abs = ops.Abs() self.reduce_mean = ops.ReduceMean() - def construct(self, adversarial_emb,input_emb,mask_tensor): + def construct(self, adversarial_emb, input_emb, mask_tensor): #像素平滑 # vert_diff = mask_tensor[:, 1:] - mask_tensor[:, :-1] # hor_diff = mask_tensor[:, :, 1:] - mask_tensor[:, :, :-1] @@ -116,11 +119,11 @@ class FaceLoss_target_attack(nn.Cell): return loss -class FaceLoss_no_target_attack(nn.Cell): +class FaceLossNoTargetAttack(nn.Cell): """The loss function of the non-target attack""" def __init__(self, target_emb): """初始化""" - super(FaceLoss_no_target_attack, self).__init__() + super(FaceLossNoTargetAttack, self).__init__() self.uniformreal = ops.UniformReal(seed=2) self.sum = ops.ReduceSum(keep_dims=False) self.norm = nn.Norm(keep_dims=True) diff --git a/examples/face_adversarial_attack/example/test.py b/examples/face_adversarial_attack/example/test.py index 95d3a0b..7bbc62b 100644 --- a/examples/face_adversarial_attack/example/test.py +++ b/examples/face_adversarial_attack/example/test.py @@ -15,7 +15,7 @@ import numpy as np import matplotlib.image as mp -from mindspore import context,Tensor +from mindspore import context, Tensor import mindspore from mindspore.dataset.vision.py_transforms import ToTensor import mindspore.dataset.vision.py_transforms as P