Model<\/h1>\n\n\n\nfrom torch import nn\nfrom torch.nn import functional as F\n\nclass LeNet(nn.Module):\n def __init__(self):\n super(LeNet, self).__init__()\n self.conv1 = nn.Conv2d(1, 10, 5)\n self.conv2 = nn.Conv2d(10, 20, 5)\n self.conv2_drop = nn.Dropout2d()\n self.fc1 = nn.Linear(320, 50)\n self.fc2 = nn.Linear(50, 10)\n\n def forward(self, x):\n x = F.relu(F.max_pool2d(self.conv1(x), 2))\n x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n x = x.view(-1, 320)\n x = F.relu(self.fc1(x))\n x = F.dropout(x, training=self.training)\n x = self.fc2(x)\n return F.log_softmax(x, dim=1)<\/code><\/pre>\n\n\n\n\u8bad\u7ec3\u8fc7\u7a0b<\/h1>\n\n\n\nimport torch\nfrom torch import nn\nfrom model import LeNet\n\n\ndef train(train_loader, epoch, device):\n model = LeNet().to(device)\n loss_fn = nn.CrossEntropyLoss()\n optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n for i in range(epoch):\n train_loss = 0\n model.train()\n\n for idx, (data, target) in enumerate(train_loader):\n data, target = data.to(device), target.to(device)\n output = model(data)\n loss = loss_fn(output, target)\n train_loss += loss.item()\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n print(f'train loss: {train_loss \/ len(train_loader)}')\n print(f'train loss: {train_loss}')\n\n\n now_loss = train_loss\n least_loss = 0\n if now_loss < least_loss:\n least_loss = now_loss\n torch.save(model.state_dict(), f='model\/LeNet_mnist_model.pth')<\/code><\/pre>\n\n\n\n\u653b\u51fb\u8fc7\u7a0b<\/h1>\n\n\n\nimport torch\nfrom matplotlib import pyplot as plt\nfrom torch.nn import functional as F\nfrom model import LeNet\nfrom train import train\nfrom torch import nn\nfrom torchvision import transforms\nfrom torchvision.datasets import MNIST\n\n# define a FGSM module\ndef fgsm_attack(image, epsilon, data_grad):\n # to find loss about derivative of input, and sign it (\u7b26\u53f7\u5316)\uff0c\u57fa\u4e8e\u7b26\u53f7\u7684\u68af\u5ea6\u6cd5\n data_grad = data_grad.sign()\n # use epsilon to create adversarial examples\n preturbed_image = image + epsilon * data_grad\n # carry out cutting work, to change numeric bigger than 1 to 1, smaller than 0 to 0 in image inner\n # prevent images from going out of bounds\n preturbed_image = torch.clamp(preturbed_image, 0, 1)\n # return adversarial examples\n return preturbed_image\n\n# define the attack process\ndef attack(model, device, test_loader, epsilon):\n correct = 0\n adv_examples = []\n\n for data, target in test_loader:\n data, target = data.to(device), target.to(device)\n data.requires_grad = True\n output = model(data)\n init_pred = output.max(1, keepdim=True)[1]\n\n for i in range(len(target)):\n if init_pred[i].item() != target[i].item():\n continue\n\n loss = F.nll_loss(output, target)\n model.zero_grad()\n loss.backward()\n\n data_grad = data.grad.data\n perturbed_data = fgsm_attack(data, epsilon, data_grad)\n output = model(perturbed_data)\n loss_after = F.nll_loss(output, target)\n # print(f'loss: {loss}, loss after: {loss_after}')\n final_pred = output.max(1, keepdim=True)[1]\n\n for i in range(len(target)):\n if final_pred[i].item() == target[i].item():\n correct += 1\n adv_examples.append((perturbed_data[i].cpu(), final_pred[i].item(), target[i].item()))\n\n final_acc = correct \/ float(40*256)\n print(f'Epsilon: {epsilon},\\t,Accuracy: {correct}\/{40*256}={final_acc*100:.2f}%')\n\n return final_acc, adv_examples\n\ndef plot_examples(adv_examples, n):\n for i in range(n):\n img, pred, true = adv_examples[i]\n plt.subplot(2, n\/\/2, i + 1)\n plt.imshow(img.squeeze().detach().numpy(), cmap='gray') # \u6dfb\u52a0 .detach().numpy()\n plt.title(f'Pred: {pred}, True: {true}')\n plt.axis('off')\n plt.tight_layout()\n plt.show()\n\n\nif __name__ == '__main__':\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n # loading dataset\n test_dataset = MNIST(root='..\/Mnist', train=False, transform=transforms.ToTensor(), download=True)\n train_dataset = MNIST(root='..\/Mnist', train=True, transform=transforms.ToTensor(), download=True)\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=256, shuffle=True)\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=256, shuffle=True)\n model = LeNet().to(device)\n # training model\n # train(train_loader, 100, device)\n # loading trained model\n pretrained_model = 'model\/LeNet_mnist_model.pth'\n model.load_state_dict(torch.load(pretrained_model, map_location='cpu'))\n model.eval()\n # attack process\n accuracies = []\n examples = []\n # epsilons = [i*0.01 for i in range(0, 51, 5)]\n # for eps in epsilons:\n # acc, ex = attack(model, device, test_loader, eps)\n # accuracies.append(acc)\n # examples.append(ex)\n acc, ex = attack(model, device, test_loader, 0.5)\n accuracies.append(acc)\n examples.append(ex)\n print(examples[1], examples[2])<\/code><\/pre>\n\n\n\n\u53c2\u8003\u8d44\u6599<\/h1>\n\n\n\n\n- Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples.<\/li>\n\n\n\n
- \u817e\u8baf\u5b89\u5168\u6731\u96c0\u5b9e\u9a8c\u5ba4. (2022). AI\u5b89\u5168<\/em>\uff1a\u6280\u672f\u4e0e\u5b9e\u6218. \u7535\u5b50\u5de5\u4e1a\u51fa\u7248\u793e. ISBN: 978-7-121-43926-1.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
\u5bf9\u6297\u6837\u672c\u5b9a\u4e49 Szegedy\u57282013\u5e74\u6700\u65e9\u63d0\u51fa\u5bf9\u6297\u6837\u672c\u7684\u6982\u5ff5\uff1a\u5728\u539f\u59cb\u6837\u672c\u5904\u52a0\u5165\u4eba\u7c7b\u65e0\u6cd5\u5bdf\u89c9\u7684\u5fae\u5c0f\u6270\u52a8\uff0c\u4f7f\u5f97\u6df1\u5ea6\u6a21\u578b\u6027\u80fd\u4e0b\u964d\uff0c\u8fd9\u79cd …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[6,7],"tags":[4,5],"class_list":["post-151","post","type-post","status-publish","format-standard","hentry","category-ai","category-7","tag-ai","tag-5"],"_links":{"self":[{"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/posts\/151","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/comments?post=151"}],"version-history":[{"count":6,"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/posts\/151\/revisions"}],"predecessor-version":[{"id":172,"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/posts\/151\/revisions\/172"}],"wp:attachment":[{"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/media?parent=151"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/categories?post=151"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/jieyekang.com\/index.php\/wp-json\/wp\/v2\/tags?post=151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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