Defense_against_adversarial_attacks.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "gOb05xDs84gA"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision import models\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.autograd import Variable\n",
        "\n",
        "import time\n",
        "import os\n",
        "import copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fgcIshYhHdU",
        "outputId": "71872812-84f9-443b-f0e7-f058bb43dff9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cC1XfhlF9mmD",
        "outputId": "bab879df-e214-4618-a615-6f468a84f848"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 170498071/170498071 [00:07<00:00, 23398651.27it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "#loading the train and test sets of CIFAR 10 and normalizing them\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
        "\n",
        "batch_size = 32\n",
        "\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
        "                                          shuffle=True, num_workers=2)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
        "                                         shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "G6pXT_x7cukI"
      },
      "outputs": [],
      "source": [
        "def gaussian(ins, mean=0, stddev=0.09):\n",
        "    noise = Variable(ins.data.new(ins.size()).normal_(mean, stddev))\n",
        "    return ins + noise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FlGSBFbL8PU",
        "outputId": "3bfcfc46-5143-4fb0-b6e0-42615624f7e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ]
        }
      ],
      "source": [
        "#Adding gaussian noise as a blackbox adversarial attack. We will be adding this to the test set as well to show reduced performance and thus, effectiveness of the model\n",
        "transform_defense = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     gaussian,\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "     ])\n",
        "\n",
        "batch_size = 32\n",
        "\n",
        "trainset_defense = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform_defense)\n",
        "trainloader_defense = torch.utils.data.DataLoader(trainset_defense, batch_size=batch_size,\n",
        "                                          shuffle=True, num_workers=2)\n",
        "\n",
        "testset_defense = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform_defense)\n",
        "testloader_defense = torch.utils.data.DataLoader(testset_defense, batch_size=batch_size,\n",
        "                                         shuffle=False, num_workers=2)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "dr2bhHuLVl_k",
        "outputId": "ddbfd484-74a6-4578-a00e-c3a3d5172443"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# functions to show an image\n",
        "\n",
        "\n",
        "def imshow(img,means,vars):\n",
        "    img = (img * vars) + means     # unnormalize\n",
        "    npimg = img.numpy()\n",
        "    # print(npimg)\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# # get some random training images\n",
        "dataiter = iter(trainloader_defense)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "# show images\n",
        "means = torch.tensor([0.485, 0.456, 0.406]).view((3,1,1))\n",
        "vars = torch.tensor([0.229, 0.224, 0.225]).view((3,1,1))\n",
        "imshow(torchvision.utils.make_grid(images),means,vars)\n",
        "# print labels\n",
        "print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rf6XRZxYB_zS",
        "outputId": "6acf692b-c16c-49e0-f13e-3e81c54feab0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "car   bird  horse dog   frog  truck dog   plane cat   horse bird  plane horse dog   car   car   truck truck ship  plane horse truck dog   dog   bird  car   car   plane cat   car   dog   dog  \n",
            "cuda:0\n"
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6WwhqR9Be4Y",
        "outputId": "c3f1f9a4-d5f7-489b-d57f-92da717d3eb8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
            "100%|██████████| 44.7M/44.7M [00:00<00:00, 316MB/s]\n"
          ]
        }
      ],
      "source": [
        "#define the resnet 18 model\n",
        "model_ft = models.resnet18(pretrained=True)\n",
        "num_ftrs = model_ft.fc.in_features\n",
        "# Here the size of each output sample is set to 2.\n",
        "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
        "model_ft.fc = nn.Linear(num_ftrs, 10)\n",
        "model_ft = model_ft.to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "# Observe that all parameters are being optimized\n",
        "optimizer = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlmcukJmEp4y",
        "outputId": "a374c6ff-3705-4f0f-b536-19610a2042f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1,   200] loss: 1.567\n",
            "[1,   400] loss: 1.185\n",
            "[1,   600] loss: 1.033\n",
            "[1,   800] loss: 0.927\n",
            "[1,  1000] loss: 0.908\n",
            "[1,  1200] loss: 0.892\n",
            "[1,  1400] loss: 0.816\n",
            "[2,   200] loss: 0.654\n",
            "[2,   400] loss: 0.669\n",
            "[2,   600] loss: 0.652\n",
            "[2,   800] loss: 0.681\n",
            "[2,  1000] loss: 0.639\n",
            "[2,  1200] loss: 0.665\n",
            "[2,  1400] loss: 0.632\n",
            "[3,   200] loss: 0.505\n",
            "[3,   400] loss: 0.508\n",
            "[3,   600] loss: 0.523\n",
            "[3,   800] loss: 0.527\n",
            "[3,  1000] loss: 0.507\n",
            "[3,  1200] loss: 0.535\n",
            "[3,  1400] loss: 0.519\n",
            "[4,   200] loss: 0.392\n",
            "[4,   400] loss: 0.395\n",
            "[4,   600] loss: 0.396\n",
            "[4,   800] loss: 0.441\n",
            "[4,  1000] loss: 0.410\n",
            "[4,  1200] loss: 0.423\n",
            "[4,  1400] loss: 0.439\n",
            "[5,   200] loss: 0.307\n",
            "[5,   400] loss: 0.330\n",
            "[5,   600] loss: 0.317\n",
            "[5,   800] loss: 0.357\n",
            "[5,  1000] loss: 0.343\n",
            "[5,  1200] loss: 0.329\n",
            "[5,  1400] loss: 0.362\n",
            "[6,   200] loss: 0.241\n",
            "[6,   400] loss: 0.246\n",
            "[6,   600] loss: 0.284\n",
            "[6,   800] loss: 0.272\n",
            "[6,  1000] loss: 0.288\n",
            "[6,  1200] loss: 0.292\n",
            "[6,  1400] loss: 0.297\n",
            "[7,   200] loss: 0.206\n",
            "[7,   400] loss: 0.199\n",
            "[7,   600] loss: 0.234\n",
            "[7,   800] loss: 0.240\n",
            "[7,  1000] loss: 0.230\n",
            "[7,  1200] loss: 0.255\n",
            "[7,  1400] loss: 0.268\n",
            "[8,   200] loss: 0.168\n",
            "[8,   400] loss: 0.181\n",
            "[8,   600] loss: 0.193\n",
            "[8,   800] loss: 0.194\n",
            "[8,  1000] loss: 0.209\n",
            "[8,  1200] loss: 0.193\n",
            "[8,  1400] loss: 0.219\n",
            "[9,   200] loss: 0.139\n",
            "[9,   400] loss: 0.139\n",
            "[9,   600] loss: 0.159\n",
            "[9,   800] loss: 0.166\n",
            "[9,  1000] loss: 0.192\n",
            "[9,  1200] loss: 0.183\n",
            "[9,  1400] loss: 0.174\n",
            "[10,   200] loss: 0.135\n",
            "[10,   400] loss: 0.118\n",
            "[10,   600] loss: 0.125\n",
            "[10,   800] loss: 0.147\n",
            "[10,  1000] loss: 0.139\n",
            "[10,  1200] loss: 0.141\n",
            "[10,  1400] loss: 0.152\n",
            "Finished Training\n"
          ]
        }
      ],
      "source": [
        "# training loop. We use the minibatch gradient descent with momentum \n",
        "for epoch in range(10):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        inputs, labels = data\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs = model_ft(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_loss += loss.item()\n",
        "        if i % 200 == 199:    # print every 2000 mini-batches\n",
        "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 200:.3f}')\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Finished Training')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "tsUA3e5ZXLZ_"
      },
      "outputs": [],
      "source": [
        "PATH = '/content/drive/MyDrive/cifar_net.pth'\n",
        "torch.save(model_ft.state_dict(), PATH)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "936txIhTX3a8"
      },
      "outputs": [],
      "source": [
        "#model_ft.load_state_dict(torch.load(\"/content/drive/MyDrive/cifar_net.pth\",map_location=device))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcgAscUAboRF",
        "outputId": "8dcd5f01-dbb1-4a6e-bc17-f23a5a6d19a4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 50K train images: 96 %\n"
          ]
        }
      ],
      "source": [
        "#accuracy on the train set\n",
        "correct = 0\n",
        "total = 0\n",
        "all_train_X = []\n",
        "all_train_labels = []\n",
        "# since we're not training, we don't need to calculate the gradients for our outputs\n",
        "with torch.no_grad():\n",
        "    for data in trainloader:\n",
        "        images, labels = data\n",
        "        images = images.to(device)\n",
        "        # calculate outputs by running images through the network\n",
        "        outputs = model_ft(images)\n",
        "        # the class with the highest energy is what we choose as prediction\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        predicted = predicted.cpu()\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print(f'Accuracy of the network on the 50K train images: {100 * correct // total} %')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "6c_DX_cdbb3k"
      },
      "outputs": [],
      "source": [
        "all_train_X = []\n",
        "all_train_labels = []\n",
        "with torch.no_grad():\n",
        "    for data in trainloader:\n",
        "        images, labels = data\n",
        "        all_train_X.append(images)\n",
        "        all_train_labels.append(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "u1_vTXJFbrp7"
      },
      "outputs": [],
      "source": [
        "all_train_X = torch.cat(all_train_X, axis=0)\n",
        "# print(all_train_X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "p4GmQqqwdNmF"
      },
      "outputs": [],
      "source": [
        "all_train_labels = torch.cat(all_train_labels)\n",
        "# print(all_train_labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iaCqjeIgGBZk",
        "outputId": "9722cbb3-e105-42e6-c1c2-a19c85a1924f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 10000 test images: 80 %\n"
          ]
        }
      ],
      "source": [
        "#performance on the test set\n",
        "correct = 0\n",
        "total = 0\n",
        "# since we're not training, we don't need to calculate the gradients for our outputs\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        images = images.to(device)\n",
        "        # calculate outputs by running images through the network\n",
        "        outputs = model_ft(images)\n",
        "        # the class with the highest energy is what we choose as prediction\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        predicted = predicted.cpu()\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "bhTwGNz8kY_C"
      },
      "outputs": [],
      "source": [
        "model_ft = model_ft.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nm8PoEnQewmI",
        "outputId": "265f3dad-8b91-4461-c29e-5bac13af86df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 10000 test images: 72 %\n"
          ]
        }
      ],
      "source": [
        "#performance on the adversarial test set, drop in accuracy by 6%\n",
        "correct = 0\n",
        "total = 0\n",
        "# since we're not training, we don't need to calculate the gradients for our outputs\n",
        "with torch.no_grad():\n",
        "    for data in testloader_defense:\n",
        "        images, labels = data\n",
        "        images = images.to(device)\n",
        "        # calculate outputs by running images through the network\n",
        "        outputs = model_ft(images)\n",
        "        # the class with the highest energy is what we choose as prediction\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        predicted = predicted.cpu()\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0YVJkJFP-BD"
      },
      "source": [
        "noise std dev vs acc vs drop \\\\\n",
        "0.09 72% 8% \\\\\n",
        "0.25 48% 32% \\\\\n",
        "0.5 31% 49% \\\\\n",
        "0.01 80% 0%\\\\"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "7xnfSRjXbjXC"
      },
      "outputs": [],
      "source": [
        "all_test_X = []\n",
        "all_test_labels = []\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        all_test_X.append(images)\n",
        "        all_test_labels.append(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "0SBIQgtEz0zu"
      },
      "outputs": [],
      "source": [
        "all_adv_test_X = []\n",
        "all_adv_test_labels = []\n",
        "with torch.no_grad():\n",
        "    for data in testloader_defense:\n",
        "        images, labels = data\n",
        "        all_adv_test_X.append(images)\n",
        "        all_adv_test_labels.append(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "5iK6Bu1Ab1UH"
      },
      "outputs": [],
      "source": [
        "all_test_X = torch.cat(all_test_X, axis=0)\n",
        "# print(all_test_X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "TWQGAG0dda6z"
      },
      "outputs": [],
      "source": [
        "all_test_labels = torch.cat(all_test_labels)\n",
        "# print(all_test_labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "j5ZglCNHBddJ"
      },
      "outputs": [],
      "source": [
        "# all_train_X = torch.tensor(all_train_X)\n",
        "# all_train_labels = torch.tensor(all_train_labels)\n",
        "# all_test_X = torch.tensor(all_test_X)\n",
        "# all_test_labels = torch.tensor(all_test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "PWD85pNgWXqA"
      },
      "outputs": [],
      "source": [
        "# # prepare to count predictions for each class\n",
        "# correct_pred = {classname: 0 for classname in classes}\n",
        "# total_pred = {classname: 0 for classname in classes}\n",
        "\n",
        "# # again no gradients needed\n",
        "# with torch.no_grad():\n",
        "#     for data in testloader:\n",
        "#         images, labels = data\n",
        "#         outputs = net(images)\n",
        "#         _, predictions = torch.max(outputs, 1)\n",
        "#         # collect the correct predictions for each class\n",
        "#         for label, prediction in zip(labels, predictions):\n",
        "#             if label == prediction:\n",
        "#                 correct_pred[classes[label]] += 1\n",
        "#             total_pred[classes[label]] += 1\n",
        "\n",
        "\n",
        "# # print accuracy for each class\n",
        "# for classname, correct_count in correct_pred.items():\n",
        "#     accuracy = 100 * float(correct_count) / total_pred[classname]\n",
        "#     print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1LpIopZi0bG"
      },
      "source": [
        "Adevrsarial Attack Generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgMJ17zBWnND",
        "outputId": "4a9c6824-fbfd-486f-dcf8-75ccdfd257b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting foolbox\n",
            "  Downloading foolbox-3.3.3-py3-none-any.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting GitPython>=3.0.7\n",
            "  Downloading GitPython-3.1.31-py3-none-any.whl (184 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m184.3/184.3 kB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting eagerpy>=0.30.0\n",
            "  Downloading eagerpy-0.30.0-py3-none-any.whl (31 kB)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.1 in /usr/local/lib/python3.10/dist-packages (from foolbox) (4.5.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from foolbox) (1.22.4)\n",
            "Requirement already satisfied: requests>=2.24.0 in /usr/local/lib/python3.10/dist-packages (from foolbox) (2.27.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from foolbox) (67.7.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from foolbox) (1.10.1)\n",
            "Collecting gitdb<5,>=4.0.1\n",
            "  Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.24.0->foolbox) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.24.0->foolbox) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.24.0->foolbox) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.24.0->foolbox) (2022.12.7)\n",
            "Collecting smmap<6,>=3.0.1\n",
            "  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: smmap, eagerpy, gitdb, GitPython, foolbox\n",
            "Successfully installed GitPython-3.1.31 eagerpy-0.30.0 foolbox-3.3.3 gitdb-4.0.10 smmap-5.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install foolbox"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "BheJTjC8qh7J"
      },
      "outputs": [],
      "source": [
        "model_ft = model_ft.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "FhBiWllHHJQx"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "M7tsqZY9i5nI"
      },
      "outputs": [],
      "source": [
        "import foolbox as fb\n",
        "preprocessing = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], axis=1)\n",
        "bounds = (-5, 5)\n",
        "fmodel = fb.PyTorchModel(model_ft, bounds=bounds, preprocessing=None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "mCLg1G5zgCLD"
      },
      "outputs": [],
      "source": [
        "def adversarial_Acc(clipped,labels,model_ft,images=None):\n",
        "    model_ft.eval()\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    predicted_labels = []\n",
        "    \n",
        "    # since we're not training, we don't need to calculate the gradients for our outputs\n",
        "    with torch.no_grad():\n",
        "        i=0\n",
        "        for images_clipped,labels_clipped in (zip(clipped,labels)):\n",
        "            images_clipped = images_clipped.unsqueeze(0)\n",
        "            \n",
        "            if images != None:\n",
        "                original_images = images[i].unsqueeze(0)\n",
        "                images_clipped = images_clipped.to(device)\n",
        "                original_images = original_images.to(device)\n",
        "                outputs_original = model_ft(original_images)\n",
        "                _, predicted_original = torch.max(outputs_original.data, 1)\n",
        "                predicted_original = predicted_original.cpu()\n",
        "\n",
        "\n",
        "            label_clipped = labels[i]\n",
        "            # calculate outputs by running images through the network\n",
        "            outputs = model_ft(images_clipped)\n",
        "            # the class with the highest energy is what we choose as prediction\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            predicted = predicted.cpu()\n",
        "            predicted_labels.append(predicted)\n",
        "            # print((predicted.item() == label_clipped.item()))\n",
        "            correct = correct + (predicted.item() == label_clipped.item())\n",
        "            i+=1\n",
        "    print(f'Accuracy of the network on the test images: {100 * (correct / i)} %')\n",
        "    if images is not None:\n",
        "        return predicted_labels, predicted_original, labels\n",
        "    else:\n",
        "        return predicted_labels, labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "swJQEViicWlA"
      },
      "outputs": [],
      "source": [
        "# LinfDeepFool Attack on 2000 elements of the test set\n",
        "attack_deepfool = fb.attacks.LinfDeepFoolAttack()\n",
        "raw_df, clipped_df, is_adv_df = attack_deepfool(fmodel, all_test_X.to(device), all_test_labels.to(device),epsilons = 0.02)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uAqAAAUoQ0h",
        "outputId": "b10cc7ba-773d-45c9-f910-b801fbec64ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the test images: 25.14 %\n"
          ]
        }
      ],
      "source": [
        "predicted_adv_df, labels_adv_df = adversarial_Acc(clipped_df, all_test_labels,model_ft)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "vt5xU70sepjM"
      },
      "outputs": [],
      "source": [
        "#FGSM Attack\n",
        "attack_fgsm = fb.attacks.FGSM()\n",
        "raw_fgsm, clipped_fgsm, is_adv_fgsm = attack_fgsm(fmodel, all_test_X.to(device),all_test_labels.to(device),epsilons=0.02) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAnFnLDLDrKo",
        "outputId": "1be7f3fa-5509-4fc0-bd17-d1f17f4c777a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the test images: 27.55 %\n"
          ]
        }
      ],
      "source": [
        "predicted_adv_fgsm, labels_adv_fgsm = adversarial_Acc(clipped_fgsm, all_test_labels,model_ft)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "xG_BDuK3EA-j"
      },
      "outputs": [],
      "source": [
        "predicted_adv_fgsm = torch.tensor(predicted_adv_fgsm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "bkq5J_oxFebk"
      },
      "outputs": [],
      "source": [
        "predicted_adv_df = torch.tensor(predicted_adv_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "sORs8UY3gWK8"
      },
      "outputs": [],
      "source": [
        "# PGD Attack on 2000 elements of the test set\n",
        "attack_deepfool = fb.attacks.LinfProjectedGradientDescentAttack()\n",
        "raw_df, clipped_df, is_adv_df = attack_deepfool(fmodel, all_test_X.to(device), all_test_labels.to(device),epsilons = 0.02)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkvHyS7Ggr4M",
        "outputId": "a5e95c15-2aa0-486c-ba4c-524a101c3dbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the test images: 24.310000000000002 %\n"
          ]
        }
      ],
      "source": [
        "predicted_adv_df, labels_adv_df = adversarial_Acc(clipped_df, all_test_labels,model_ft)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "6klgLdRegu48"
      },
      "outputs": [],
      "source": [
        "# CW Attack on 200 elements of the test set\n",
        "attack_deepfool = fb.attacks.L2CarliniWagnerAttack()\n",
        "raw_df, clipped_df, is_adv_df = attack_deepfool(fmodel, all_test_X[:200].to(device), all_test_labels[:200].to(device),epsilons = 0.02)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84elkQ1Og-wN",
        "outputId": "dc12620e-66ca-4184-cdf8-afe4d49a065d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the test images: 57.49999999999999 %\n"
          ]
        }
      ],
      "source": [
        "predicted_adv_df, labels_adv_df = adversarial_Acc(clipped_df, all_test_labels[:200],model_ft)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyEGLorywhmU"
      },
      "source": [
        "eps 0.002 - Accuracy of the network on the test images: 54.31 % deepfool\n",
        "54.36 % fgsm 54.75 % pgd 58.5 CW\n",
        "\n",
        "eps 0.02 - Accuracy of the network on the test images: 25.14 % % deepfool\n",
        "27.55 % % fgsm 24.31 % pgd 57.5 CW\n",
        "\n",
        "eps 0.2 - Accuracy of the network on the test images: 0.01 % deepfool\n",
        "6.67 % fgsm 0.6 % pgd 53.5 CW"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAPU9gTyk2D0"
      },
      "source": [
        "Adversarial Defense - Adding adversarial images from training set to training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vO0fWEUxnIHP",
        "outputId": "5e67529b-5621-4fc7-f779-1fe51d4d0dcd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n"
          ]
        }
      ],
      "source": [
        "#training a model on the train data along with the train data with added noise\n",
        "model_ft_defense = models.resnet18(pretrained=True)\n",
        "num_ftrs = model_ft_defense.fc.in_features\n",
        "# Here the size of each output sample is set to 2.\n",
        "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n",
        "model_ft_defense.fc = nn.Linear(num_ftrs, 10)\n",
        "model_ft_defense = model_ft_defense.to(device)\n",
        "criterion_defense = nn.CrossEntropyLoss()\n",
        "\n",
        "# Observe that all parameters are being optimized\n",
        "# optimizer_defense1 = optim.RMSprop(model_ft_defense.parameters(), lr=0.01)\n",
        "optimizer_defense2 = optim.SGD(model_ft_defense.parameters(), lr=0.001, momentum=0.9)\n",
        "# optimizer_defense3 = optim.SGD(model_ft_defense.parameters(), lr=0.0001, momentum=0.9)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fiW7WxpJONau",
        "outputId": "a95a3f91-83f9-4484-c5f8-9465f1ea31a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1,   200] loss: 1.642\n",
            "[1,   400] loss: 1.249\n",
            "[1,   600] loss: 1.144\n",
            "[1,   800] loss: 1.042\n",
            "[1,  1000] loss: 1.022\n",
            "[1,  1200] loss: 0.978\n",
            "[1,  1400] loss: 0.911\n",
            "[2,   200] loss: 0.824\n",
            "[2,   400] loss: 0.817\n",
            "[2,   600] loss: 0.811\n",
            "[2,   800] loss: 0.799\n",
            "[2,  1000] loss: 0.806\n",
            "[2,  1200] loss: 0.775\n",
            "[2,  1400] loss: 0.792\n",
            "[3,   200] loss: 0.698\n",
            "[3,   400] loss: 0.684\n",
            "[3,   600] loss: 0.704\n",
            "[3,   800] loss: 0.694\n",
            "[3,  1000] loss: 0.663\n",
            "[3,  1200] loss: 0.655\n",
            "[3,  1400] loss: 0.718\n",
            "[4,   200] loss: 0.604\n",
            "[4,   400] loss: 0.607\n",
            "[4,   600] loss: 0.606\n",
            "[4,   800] loss: 0.601\n",
            "[4,  1000] loss: 0.615\n",
            "[4,  1200] loss: 0.619\n",
            "[4,  1400] loss: 0.603\n",
            "[5,   200] loss: 0.533\n",
            "[5,   400] loss: 0.533\n",
            "[5,   600] loss: 0.542\n",
            "[5,   800] loss: 0.544\n",
            "[5,  1000] loss: 0.535\n",
            "[5,  1200] loss: 0.537\n",
            "[5,  1400] loss: 0.543\n",
            "[6,   200] loss: 0.463\n",
            "[6,   400] loss: 0.466\n",
            "[6,   600] loss: 0.474\n",
            "[6,   800] loss: 0.465\n",
            "[6,  1000] loss: 0.474\n",
            "[6,  1200] loss: 0.491\n",
            "[6,  1400] loss: 0.498\n",
            "[7,   200] loss: 0.430\n",
            "[7,   400] loss: 0.436\n",
            "[7,   600] loss: 0.447\n",
            "[7,   800] loss: 0.443\n",
            "[7,  1000] loss: 0.437\n",
            "[7,  1200] loss: 0.462\n",
            "[7,  1400] loss: 0.464\n",
            "[8,   200] loss: 0.380\n",
            "[8,   400] loss: 0.386\n",
            "[8,   600] loss: 0.384\n",
            "[8,   800] loss: 0.400\n",
            "[8,  1000] loss: 0.393\n",
            "[8,  1200] loss: 0.421\n",
            "[8,  1400] loss: 0.396\n",
            "[9,   200] loss: 0.333\n",
            "[9,   400] loss: 0.359\n",
            "[9,   600] loss: 0.339\n",
            "[9,   800] loss: 0.396\n",
            "[9,  1000] loss: 0.368\n",
            "[9,  1200] loss: 0.373\n",
            "[9,  1400] loss: 0.406\n",
            "[10,   200] loss: 0.309\n",
            "[10,   400] loss: 0.316\n",
            "[10,   600] loss: 0.331\n",
            "[10,   800] loss: 0.311\n",
            "[10,  1000] loss: 0.370\n",
            "[10,  1200] loss: 0.342\n",
            "[10,  1400] loss: 0.354\n",
            "Finished Training\n"
          ]
        }
      ],
      "source": [
        "#training loop\n",
        "for epoch in range(10):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, data in enumerate(trainloader_defense, 0):\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        inputs, labels = data\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer_defense2.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        outputs = model_ft_defense(inputs)\n",
        "        loss = criterion_defense(outputs, labels)\n",
        "        loss.backward()\n",
        "\n",
        "        optimizer_defense2.step()\n",
        "\n",
        "        # print statistics\n",
        "        running_loss += loss.item()\n",
        "        if i % 200 == 199:    # print every 2000 mini-batches\n",
        "            print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 200:.3f}')\n",
        "            running_loss = 0.0\n",
        "\n",
        "print('Finished Training')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-PTLqa7r1vj4"
      },
      "outputs": [],
      "source": [
        "torch.save(model_ft_defense.cpu().state_dict(), \"/content/drive/MyDrive/cifar_net_w_adv.pth\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PpgXSkbB2PmA"
      },
      "outputs": [],
      "source": [
        "model_t_defense = model_ft_defense.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtWmRv7Z2F2H",
        "outputId": "64eca23a-d01f-4a4c-890c-7799e5b54600"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 10000 test images: 80 %\n"
          ]
        }
      ],
      "source": [
        "#test the adversarial defense model on original test set\n",
        "correct = 0\n",
        "total = 0\n",
        "# since we're not training, we don't need to calculate the gradients for our outputs\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        images = images.to(device)\n",
        "        # calculate outputs by running images through the network\n",
        "        outputs = model_ft_defense(images)\n",
        "        # the class with the highest energy is what we choose as prediction\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        predicted = predicted.cpu()\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uE_JT0oryeRg",
        "outputId": "28e7731e-75ac-4818-8d65-82e448d6db90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 10000 test images: 77 %\n"
          ]
        }
      ],
      "source": [
        "#test the adversarial defense on the adversarial test set\n",
        "correct = 0\n",
        "total = 0\n",
        "# since we're not training, we don't need to calculate the gradients for our outputs\n",
        "with torch.no_grad():\n",
        "    for data in testloader_defense:\n",
        "        images, labels = data\n",
        "        images = images.to(device)\n",
        "        # calculate outputs by running images through the network\n",
        "        outputs = model_ft_defense(images)\n",
        "        # the class with the highest energy is what we choose as prediction\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        predicted = predicted.cpu()\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CaF-lzCxH0Vm"
      },
      "source": [
        "Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "APhg8rCjHfWC"
      },
      "outputs": [],
      "source": [
        "means = torch.tensor([0.485, 0.456, 0.406]).view((3,1,1))\n",
        "vars = torch.tensor([0.229, 0.224, 0.225]).view((3,1,1))\n",
        "idx=16\n",
        "img_df = ((clipped_df[idx].cpu()*vars)+means).numpy().transpose((1,2,0))\n",
        "img_fgsm = ((clipped_df[idx].cpu()*vars)+means).numpy().transpose((1,2,0))\n",
        "img_ori = ((all_test_X[idx].cpu()*vars)+means).numpy().transpose((1,2,0))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cvgaUUmsIN9O"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "DR7HwgryIXnz",
        "outputId": "b55619c8-37d5-4307-88a8-0b5b6bd8327d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dog\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(img_ori)\n",
        "print(classes[all_test_labels[idx].item()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "LfOUs26pIB2W",
        "outputId": "95bd2dca-7d8b-47c5-c434-dc5bb3d5db96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cat\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(img_df)\n",
        "print(classes[predicted_adv_df[idx].item()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "3hWpAI5NwLQQ",
        "outputId": "d476151a-6370-4f5b-947e-45161bdfd923"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cat\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(img_fgsm)\n",
        "print(classes[predicted_adv_fgsm[idx].item()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "cDhOIUmDMmhr",
        "outputId": "cdcfab0c-0b9d-4806-875c-bf8ff29d0467"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe311cefd90>"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(np.abs(img_ori - img_fgsm))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLwBj9oQPV0p",
        "outputId": "14c9ff7d-bb57-4000-db77-9c68622bf37e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.abs(img_ori - img_fgsm).any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ms3k4kOH0bUp"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}