Adversarial_Training_Example_with_FGSM.ipynb

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      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "\n",
        "# Example data (replace with your actual data)\n",
        "X_train = torch.randn(100, 3, 32, 32)  # 100 samples, 3 channels, 32x32 image\n",
        "y_train = torch.randint(0, 10, (100,))  # 100 labels, for 10 classes\n",
        "\n",
        "# Create dataset and dataloader\n",
        "dataset = TensorDataset(X_train, y_train)\n",
        "dataloader = DataLoader(dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "# Define your model (example model)\n",
        "class MyModel(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MyModel, self).__init__()\n",
        "        self.conv1 = torch.nn.Conv2d(3, 10, kernel_size=5)\n",
        "        self.fc1 = torch.nn.Linear(10*28*28, 10)  # Assuming input size 32x32\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.conv1(x))\n",
        "        x = x.view(-1, 10*28*28)\n",
        "        x = self.fc1(x)\n",
        "        return F.log_softmax(x, dim=1)\n",
        "\n",
        "model = MyModel()\n",
        "optimizer = torch.optim.Adam(model.parameters())\n",
        "\n",
        "def fgsm_attack(model, inputs, labels, epsilon=0.1):\n",
        "    inputs.requires_grad = True\n",
        "    outputs = model(inputs)\n",
        "    loss = F.nll_loss(outputs, labels)\n",
        "    model.zero_grad()\n",
        "    loss.backward()\n",
        "    perturbed_inputs = inputs + epsilon * inputs.grad.sign()\n",
        "    return perturbed_inputs\n",
        "\n",
        "# Train on adversarial examples\n",
        "for data, labels in dataloader:\n",
        "    optimizer.zero_grad()  # Clear gradients\n",
        "    adv_data = fgsm_attack(model, data, labels)\n",
        "    output = model(adv_data)\n",
        "    loss = F.cross_entropy(output, labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()"
      ],
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