notebooks/attack_graphite_whitebox.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "txooXPfhaCK0",
      "metadata": {
        "id": "txooXPfhaCK0"
      },
      "source": [
        "# ART GRAPHITE Whitebox Attack"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "CS3f9y-iq2rW",
      "metadata": {
        "id": "CS3f9y-iq2rW"
      },
      "source": [
        "Install Kornia, an open source Differentiable Computer Vision Library for PyTorch\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Xnp0CXChiuI1",
      "metadata": {
        "id": "Xnp0CXChiuI1"
      },
      "outputs": [],
      "source": [
        "pip install kornia"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "ym2ZL1AEqzqM",
      "metadata": {
        "id": "ym2ZL1AEqzqM"
      },
      "source": [
        "Setup and load the data and train a model to test on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "AO9XssuBaCK6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AO9XssuBaCK6",
        "outputId": "8fde263e-3024-497a-8e0a-d7f6c143cf0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy on benign test examples: 97.6%\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from art.attacks.evasion import GRAPHITEWhiteboxPyTorch\n",
        "from art.estimators.classification import PyTorchClassifier\n",
        "from art.utils import load_mnist\n",
        "\n",
        "\n",
        "# Step 1: Load the MNIST dataset\n",
        "\n",
        "(x_train, y_train), (x_test, y_test), min_pixel_value, max_pixel_value = load_mnist()\n",
        "\n",
        "# Step 1a: Swap axes to PyTorch's NCHW format\n",
        "\n",
        "x_train = np.transpose(x_train, (0, 3, 1, 2)).astype(np.float32)\n",
        "x_test = np.transpose(x_test, (0, 3, 1, 2)).astype(np.float32)\n",
        "\n",
        "# Step 2: Create the model\n",
        "\n",
        "model = nn.Sequential(\n",
        "    nn.Conv2d(1, 4, 5), nn.ReLU(), nn.MaxPool2d(2, 2),\n",
        "    nn.Conv2d(4, 10, 5), nn.ReLU(), nn.MaxPool2d(2, 2),\n",
        "    nn.Flatten(),\n",
        "    nn.Linear(4*4*10, 100),\n",
        "    nn.Linear(100, 10)\n",
        ")\n",
        "# Step 2a: Define the loss function and the optimizer\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.01)\n",
        "\n",
        "# Step 3: Create the ART classifier\n",
        "\n",
        "classifier = PyTorchClassifier(\n",
        "    model=model,\n",
        "    clip_values=(min_pixel_value, max_pixel_value),\n",
        "    loss=criterion,\n",
        "    optimizer=optimizer,\n",
        "    input_shape=(1, 28, 28),\n",
        "    nb_classes=10,\n",
        ")\n",
        "\n",
        "# Step 4: Train the ART classifier\n",
        "\n",
        "classifier.fit(x_train, y_train, batch_size=64, nb_epochs=5)\n",
        "\n",
        "# Step 5: Evaluate the ART classifier on benign test examples\n",
        "\n",
        "predictions = classifier.predict(x_test)\n",
        "accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)\n",
        "print(\"Accuracy on benign test examples: {}%\".format(accuracy * 100))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "wsOpmCgAaCK9",
      "metadata": {
        "id": "wsOpmCgAaCK9"
      },
      "source": [
        "# Get Init Image\n",
        "\n",
        "Second, load an initialization image to attack."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "qAKzUShfaCK9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "qAKzUShfaCK9",
        "outputId": "1626a4f0-1918-4fac-b2dc-e2ebf7719e37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Init image is:  7\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Target label is:  2\n"
          ]
        }
      ],
      "source": [
        "init_image = x_test[0, :, :, :]\n",
        "\n",
        "print(\"Init image is: \", np.argmax(classifier.predict(np.array([init_image]))[0]))\n",
        "plt.imshow((init_image.squeeze() * 255).astype(np.uint))\n",
        "plt.show()\n",
        "\n",
        "target_label_index = np.argmax(y_test[1])\n",
        "target_label = target_label_index.item()\n",
        "print(\"Target label is: \", target_label)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "wvslQXLWaCK-",
      "metadata": {
        "id": "wvslQXLWaCK-"
      },
      "source": [
        "# Run GRAPHITE Whitebox Attack\n",
        "\n",
        "Finally, run an attack on a GRAHPITEWhiteboxPyTorch instance. We set the network size to 28 x 28 for MNIST and set the inner EoT PGD steps to 50. The attack is shown at the end."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "KSbz8rOhaCK-",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "KSbz8rOhaCK-",
        "outputId": "94bbbbc9-4dae-4972-b029-b29b98805e87",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted label is:  2\n"
          ]
        }
      ],
      "source": [
        "# Step 6: Generate adversarial test examples\n",
        "attack = GRAPHITEWhiteboxPyTorch(\n",
        "    classifier,\n",
        "    (28, 28),\n",
        "    patch_removal_size = 4,\n",
        "    patch_removal_interval = 1,\n",
        "    steps = 50\n",
        ")\n",
        "\n",
        "x_test_adv = attack.generate(x=x_test[0, :, :, :][np.newaxis, :, :, :], y=y_test[1][np.newaxis, :], mask=None)\n",
        "plt.imshow(x_test_adv[0,...].squeeze())\n",
        "plt.show()\n",
        "\n",
        "predicted_label = np.argmax(classifier.predict(np.array([x_test_adv[0, ...]]))[0])\n",
        "print(\"Predicted label is: \", predicted_label)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}