AI-Red-Teaming-CSCD94/first-order-attacks/fgsm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4da2c53a-ad93-4a21-b5b2-ae26b1efc72b",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87540fd0-0014-4bc9-9b0e-2fb35d1a9489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1: Avg Loss = 0.1567, Test Accuracy = 98.37%\n",
      "Baseline test accuracy: 98.37%\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn, Tensor\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# Import common utilities from HTB Evasion Library\n",
    "from htb_ai_library import (\n",
    "    set_reproducibility,\n",
    "    SimpleCNN,\n",
    "    get_mnist_loaders,\n",
    "    mnist_denormalize,\n",
    "    train_model,\n",
    "    evaluate_accuracy\n",
    ")\n",
    "\n",
    "# Configure reproducibility\n",
    "set_reproducibility(1337)\n",
    "\n",
    "# Configure computation device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Prepare data loaders using library function (normalized space)\n",
    "train_loader, test_loader = get_mnist_loaders(batch_size=128, normalize=True)\n",
    "\n",
    "# Initialize model using library's SimpleCNN\n",
    "model = SimpleCNN().to(device)\n",
    "\n",
    "# Train the model using library function\n",
    "trained_model = train_model(model, train_loader, test_loader, epochs=1, device=device)\n",
    "\n",
    "# Evaluate baseline accuracy using library function\n",
    "baseline_acc = evaluate_accuracy(trained_model, test_loader, device)\n",
    "print(f\"Baseline test accuracy: {baseline_acc:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "652c65d8-4f66-49d9-b56b-625d824ec830",
   "metadata": {},
   "source": [
    "# Core Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8986fb35-25cc-42e2-bdae-7bfd178df181",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _forward_and_loss(model: nn.Module, x: Tensor, y: Tensor) -> tuple[Tensor, Tensor]:\n",
    "    \"\"\"Forward pass and cross-entropy loss without side effects.\n",
    "\n",
    "    Args:\n",
    "        model: Neural network classifier\n",
    "        x: Input images tensor\n",
    "        y: Target labels tensor\n",
    "\n",
    "    Returns:\n",
    "        tuple[Tensor, Tensor]: Model logits and scalar loss value\n",
    "    \"\"\"\n",
    "    if getattr(model, \"training\", False):\n",
    "        raise RuntimeError(\"Expected model.eval() for attack computations to avoid BN/Dropout state updates\")\n",
    "    logits = model(x)\n",
    "    loss = F.cross_entropy(logits, y)\n",
    "    return logits, loss\n",
    "\n",
    "def _input_gradient(model: nn.Module, x: Tensor, y: Tensor) -> Tensor:\n",
    "    \"\"\"Return gradient of loss with respect to input tensor x.\n",
    "\n",
    "    Args:\n",
    "        model: Neural network in evaluation mode\n",
    "        x: Input images to compute gradients for\n",
    "        y: True labels for loss computation\n",
    "\n",
    "    Returns:\n",
    "        Tensor: Gradient tensor with same shape as x\n",
    "    \"\"\"\n",
    "    x_req = x.clone().detach().requires_grad_(True)\n",
    "    _, loss = _forward_and_loss(model, x_req, y)\n",
    "    model.zero_grad(set_to_none=True)\n",
    "    loss.backward()\n",
    "    return x_req.grad.detach()\n",
    "\n",
    "def fgsm_attack(model: nn.Module,\n",
    "                images: Tensor,\n",
    "                labels: Tensor,\n",
    "                epsilon: float,\n",
    "                targeted: bool = False) -> Tensor:\n",
    "\n",
    "    # Valid normalized range for MNIST\n",
    "    MNIST_NORM_MIN = (0.0 - 0.1307) / 0.3081\n",
    "    MNIST_NORM_MAX = (1.0 - 0.1307) / 0.3081\n",
    "\n",
    "    if epsilon < 0:\n",
    "        raise ValueError(\"epsilon must be non-negative\")\n",
    "    if not images.is_floating_point():\n",
    "        raise ValueError(\"images must be floating point tensors\")\n",
    "\n",
    "    grad = _input_gradient(model, images, labels)\n",
    "    step_dir = -1.0 if targeted else 1.0\n",
    "    x_adv = images + step_dir * epsilon * grad.sign()\n",
    "    x_adv = torch.clamp(x_adv, MNIST_NORM_MIN, MNIST_NORM_MAX)\n",
    "    return x_adv.detach()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2c7e2d-cdf4-4c4d-b2f9-f267a0b54e81",
   "metadata": {},
   "source": [
    "# Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "605666f2-028e-4db1-8c2d-6971a1d7f357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FGSM flips (first batch): 69.53%\n"
     ]
    }
   ],
   "source": [
    "images, labels = next(iter(test_loader))\n",
    "images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "model.eval()\n",
    "# Epsilon in normalized space (≈0.25 in pixel space)\n",
    "epsilon = 0.8\n",
    "with torch.no_grad():\n",
    "    clean_pred = model(images).argmax(dim=1)\n",
    "\n",
    "x_adv = fgsm_attack(model, images, labels, epsilon)\n",
    "with torch.no_grad():\n",
    "    adv_pred = model(x_adv).argmax(dim=1)\n",
    "\n",
    "originally_correct = (clean_pred == labels)\n",
    "flipped = (adv_pred != labels) & originally_correct\n",
    "success = flipped.sum().item() / max(int(originally_correct.sum().item()), 1)\n",
    "print(f\"FGSM flips (first batch): {success:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d3fcf0-9d58-411f-a261-6ad8e44c23ce",
   "metadata": {},
   "source": [
    "# Pixel - space FGSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9fcd9d9-53e8-495d-bcb2-2b86844aef97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _norm_params(images: Tensor, mean: list, std: list) -> tuple[Tensor, Tensor]:\n",
    "    \"\"\"Convert normalization parameters to broadcastable tensors.\n",
    "\n",
    "    Args:\n",
    "        images: Input images tensor with shape (N, C, H, W)\n",
    "        mean: Normalization mean per channel as list\n",
    "        std: Normalization std per channel as list\n",
    "\n",
    "    Returns:\n",
    "        tuple[Tensor, Tensor]: Mean and std tensors with shape (1, C, 1, 1)\n",
    "    \"\"\"\n",
    "    device, dtype, C = images.device, images.dtype, images.shape[1]\n",
    "    mean_t = torch.tensor(mean, device=device, dtype=dtype).view(1, -1, 1, 1)\n",
    "    std_t = torch.tensor(std, device=device, dtype=dtype).view(1, -1, 1, 1)\n",
    "    if mean_t.shape[1] != C or std_t.shape[1] != C:\n",
    "        raise ValueError(\"mean/std channels must match images\")\n",
    "    return mean_t, std_t\n",
    "\n",
    "def fgsm_pixel_space(model: nn.Module,\n",
    "                     images: Tensor,\n",
    "                     labels: Tensor,\n",
    "                     epsilon: float,\n",
    "                     mean: list,\n",
    "                     std: list,\n",
    "                     targeted: bool = False) -> Tensor:\n",
    "    \"\"\"FGSM for pixel-space inputs attacking normalized models.\n",
    "\n",
    "    This variant accepts images in [0,1] pixel space rather than normalized\n",
    "    space. It normalizes inputs internally for the model, converts gradients\n",
    "    back to pixel space, and returns adversarials in [0,1] pixel space.\n",
    "\n",
    "    Args:\n",
    "        model: Model expecting normalized inputs\n",
    "        images: Clean images in [0,1] pixel space (unnormalized)\n",
    "        labels: Target labels\n",
    "        epsilon: Max perturbation in pixel space (e.g., 8/255)\n",
    "        mean: Normalization mean per channel\n",
    "        std: Normalization std per channel\n",
    "        targeted: If True, minimize loss towards labels\n",
    "\n",
    "    Returns:\n",
    "        Tensor: Adversarial images in [0,1] pixel space (unnormalized)\n",
    "    \"\"\"\n",
    "    mean_t, std_t = _norm_params(images, mean, std)\n",
    "    x = images.clone().detach()\n",
    "    x_norm = (x - mean_t) / std_t\n",
    "    x_norm.requires_grad_(True)\n",
    "\n",
    "    _, loss = _forward_and_loss(model, x_norm, labels)\n",
    "    model.zero_grad(set_to_none=True)\n",
    "    loss.backward()\n",
    "\n",
    "    # Convert gradient from normalized space to image space\n",
    "    grad_img = x_norm.grad / std_t\n",
    "    step_dir = -1.0 if targeted else 1.0\n",
    "    x_adv = torch.clamp(x + step_dir * epsilon * grad_img.sign(), 0.0, 1.0)\n",
    "    return x_adv.detach()\n",
    "\n",
    "# Example: Starting with pixel-space images\n",
    "epsilon_px = 8 / 255  # pixel-space epsilon (≈0.031)\n",
    "mean, std = [0.1307], [0.3081]\n",
    "\n",
    "# Denormalize existing normalized images to get pixel-space images\n",
    "mean_t, std_t = _norm_params(images, mean, std)\n",
    "pixel_images = images * std_t + mean_t\n",
    "pixel_images = torch.clamp(pixel_images, 0.0, 1.0)\n",
    "\n",
    "# Attack in pixel space\n",
    "x_adv_pixel = fgsm_pixel_space(model, pixel_images, labels, epsilon_px, mean, std)\n",
    "\n",
    "# x_adv_pixel is in [0,1] and can be displayed or saved directly\n",
    "# If you need to pass to the model again, normalize it first:\n",
    "x_adv_norm = (x_adv_pixel - mean_t) / std_t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26be0c0-550f-43fc-b1ac-37efc4de2331",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd04bfe2-d396-4af1-88cb-d7f0c134348a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clean_accuracy: 1.0000\n",
      "adversarial_accuracy: 0.3047\n",
      "attack_success_rate: 0.6953\n",
      "avg_clean_confidence: 0.9854\n",
      "avg_adv_confidence: 0.2756\n",
      "avg_confidence_drop: 0.7099\n",
      "avg_l2_perturbation: 17.2031\n",
      "max_linf_perturbation: 0.8000\n"
     ]
    }
   ],
   "source": [
    "from typing import Dict\n",
    "\n",
    "def evaluate_attack(model: nn.Module,\n",
    "                   clean_images: Tensor,\n",
    "                   adversarial_images: Tensor,\n",
    "                   true_labels: Tensor) -> Dict[str, float]:\n",
    "    \"\"\"Compute accuracy, success rate, confidence shift, and norms.\n",
    "\n",
    "    Args:\n",
    "        model: Evaluated classifier in evaluation mode\n",
    "        clean_images: Clean inputs in the model's expected domain (e.g., normalized MNIST)\n",
    "        adversarial_images: Adversarial counterparts in the same domain as `clean_images`\n",
    "        true_labels: Ground-truth labels\n",
    "\n",
    "    Returns:\n",
    "        Dict[str, float]: Aggregated metrics summarizing attack impact\n",
    "    \"\"\"\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        clean_logits = model(clean_images)\n",
    "        adv_logits = model(adversarial_images)\n",
    "\n",
    "        clean_probs = F.softmax(clean_logits, dim=1)\n",
    "        adv_probs = F.softmax(adv_logits, dim=1)\n",
    "\n",
    "        clean_pred = clean_logits.argmax(dim=1)\n",
    "        adv_pred = adv_logits.argmax(dim=1)\n",
    "\n",
    "        clean_correct = (clean_pred == true_labels)\n",
    "        adv_correct = (adv_pred == true_labels)\n",
    "\n",
    "        originally_correct = clean_correct\n",
    "        flipped = (~adv_correct) & originally_correct\n",
    "\n",
    "        conf_clean = clean_probs.gather(1, true_labels.view(-1, 1)).squeeze(1)\n",
    "        conf_adv = adv_probs.gather(1, true_labels.view(-1, 1)).squeeze(1)\n",
    "\n",
    "        l2 = (adversarial_images - clean_images).view(clean_images.size(0), -1).norm(p=2, dim=1)\n",
    "        linf = (adversarial_images - clean_images).abs().amax()\n",
    "\n",
    "        return {\n",
    "            \"clean_accuracy\": clean_correct.float().mean().item(),\n",
    "            \"adversarial_accuracy\": adv_correct.float().mean().item(),\n",
    "            # Success rate among originally correct samples only\n",
    "            \"attack_success_rate\": (\n",
    "                flipped.float().sum() / originally_correct.float().sum().clamp_min(1.0)\n",
    "            ).item(),\n",
    "            \"avg_clean_confidence\": conf_clean.mean().item(),\n",
    "            \"avg_adv_confidence\": conf_adv.mean().item(),\n",
    "            \"avg_confidence_drop\": (conf_clean - conf_adv).mean().item(),\n",
    "            \"avg_l2_perturbation\": l2.mean().item(),\n",
    "            \"max_linf_perturbation\": linf.item(),\n",
    "        }\n",
    "\n",
    "metrics = evaluate_attack(model, images, x_adv, labels)\n",
    "for k, v in metrics.items():\n",
    "    print(f\"{k}: {v:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c653cdf-5d4a-4255-81d1-c26e75c13abf",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "562839cd-e6fd-4698-8045-356420af9a48",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5838/414685490.py:129: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(rect=(0, 0, 1, 0.93))\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Colors imported from library\n",
    "from htb_ai_library import (\n",
    "    HTB_GREEN, NODE_BLACK, HACKER_GREY, WHITE,\n",
    "    AZURE, NUGGET_YELLOW, MALWARE_RED, VIVID_PURPLE, AQUAMARINE\n",
    ")\n",
    "\n",
    "def _style_axes(ax: plt.Axes) -> None:\n",
    "    \"\"\"Apply Hack The Box dark theme to an axes instance.\n",
    "\n",
    "    Args:\n",
    "        ax: Matplotlib axes to style\n",
    "    \"\"\"\n",
    "    ax.set_facecolor(NODE_BLACK)\n",
    "    ax.tick_params(colors=HACKER_GREY)\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_color(HACKER_GREY)\n",
    "    ax.grid(True, color=HACKER_GREY, linestyle=\"--\", alpha=0.25)\n",
    "\n",
    "def visualize_attack(model: nn.Module,\n",
    "                    image: Tensor,\n",
    "                    label: Tensor,\n",
    "                    make_adv,\n",
    "                    title: str,\n",
    "                    num_classes: int = 10,\n",
    "                    targeted: bool = False,\n",
    "                    target_class: int | None = None) -> None:\n",
    "    \"\"\"HTB-styled visualization for adversarial examples.\n",
    "\n",
    "    Args:\n",
    "        model: Classifier in evaluation mode\n",
    "        image: Single image in normalized space, shape (C,H,W)\n",
    "        label: Scalar true label tensor\n",
    "        make_adv: Callable (model, image_batch, label_batch) -> adv_batch in normalized space\n",
    "        title: Figure title\n",
    "        num_classes: Number of classes to show in probability bars\n",
    "        targeted: Whether the attack is targeted\n",
    "        target_class: Optional target class to annotate\n",
    "    \"\"\"\n",
    "    model.eval()\n",
    "    dev = next(model.parameters()).device\n",
    "    image_dev = image.to(dev)\n",
    "    label_dev = label.to(dev)\n",
    "\n",
    "    # Compute clean predictions\n",
    "    with torch.no_grad():\n",
    "        clean_probs = F.softmax(model(image_dev.unsqueeze(0)), dim=1).squeeze(0)\n",
    "        clean_pred = int(clean_probs.argmax().item())\n",
    "\n",
    "    # Generate adversarial example\n",
    "    x_adv_dev = make_adv(model, image_dev.unsqueeze(0), label_dev.unsqueeze(0)).squeeze(0)\n",
    "    perturbation_dev = x_adv_dev - image_dev\n",
    "\n",
    "    # Compute adversarial predictions\n",
    "    with torch.no_grad():\n",
    "        adv_probs = F.softmax(model(x_adv_dev.unsqueeze(0)), dim=1).squeeze(0)\n",
    "        adv_pred = int(adv_probs.argmax().item())\n",
    "\n",
    "    # Denormalize for visualization\n",
    "    image_vis = mnist_denormalize(image_dev.unsqueeze(0)).squeeze(0).detach().cpu()\n",
    "    x_adv_vis = mnist_denormalize(x_adv_dev.unsqueeze(0)).squeeze(0).detach().cpu()\n",
    "    perturbation_vis = (x_adv_vis - image_vis)\n",
    "\n",
    "    # Create figure with grid layout\n",
    "    fig = plt.figure(figsize=(16, 10), facecolor=NODE_BLACK)\n",
    "    gs = fig.add_gridspec(2, 3, hspace=0.35, wspace=0.35)\n",
    "\n",
    "    # Original image panel\n",
    "    ax1 = fig.add_subplot(gs[0, 0])\n",
    "    _style_axes(ax1)\n",
    "    if image_vis.shape[0] == 1:\n",
    "        ax1.imshow(image_vis.squeeze(0), cmap='gray', vmin=0, vmax=1)\n",
    "    else:\n",
    "        ax1.imshow(image_vis.permute(1, 2, 0))\n",
    "    ax1.set_title(f\"Original | class={clean_pred} | p={clean_probs[clean_pred]:.2%}\",\n",
    "                  color=HTB_GREEN, fontweight=\"bold\")\n",
    "    ax1.set_xticks([])\n",
    "    ax1.set_yticks([])\n",
    "\n",
    "    # Adversarial image panel\n",
    "    ax2 = fig.add_subplot(gs[0, 1])\n",
    "    _style_axes(ax2)\n",
    "    if x_adv_vis.shape[0] == 1:\n",
    "        ax2.imshow(x_adv_vis.squeeze(0), cmap='gray', vmin=0, vmax=1)\n",
    "    else:\n",
    "        ax2.imshow(x_adv_vis.permute(1, 2, 0))\n",
    "    title_color = MALWARE_RED if adv_pred != int(label.item()) else HTB_GREEN\n",
    "    adv_title = f\"Adversarial | class={adv_pred} | p={adv_probs[adv_pred]:.2%}\"\n",
    "    if targeted and target_class is not None:\n",
    "        adv_title += f\" | target={target_class}\"\n",
    "    ax2.set_title(adv_title, color=title_color, fontweight=\"bold\")\n",
    "    ax2.set_xticks([])\n",
    "    ax2.set_yticks([])\n",
    "\n",
    "    # Perturbation panel (scaled for visibility)\n",
    "    ax3 = fig.add_subplot(gs[0, 2])\n",
    "    _style_axes(ax3)\n",
    "    pert_scaled = (perturbation_vis * 10 + 0.5).clamp(0, 1)\n",
    "    if pert_scaled.shape[0] == 1:\n",
    "        ax3.imshow(pert_scaled.squeeze(0), cmap='gray', vmin=0, vmax=1)\n",
    "    else:\n",
    "        ax3.imshow(pert_scaled.permute(1, 2, 0))\n",
    "    ax3.set_title(\"Perturbation (x10)\", color=NUGGET_YELLOW, fontweight=\"bold\")\n",
    "    ax3.set_xticks([])\n",
    "    ax3.set_yticks([])\n",
    "\n",
    "   # Class probability comparison\n",
    "    ax4 = fig.add_subplot(gs[1, :])\n",
    "    _style_axes(ax4)\n",
    "    x = np.arange(num_classes)\n",
    "    width = 0.4\n",
    "    ax4.bar(x - width/2, clean_probs[:num_classes].cpu(), width,\n",
    "            color=AZURE, label=\"clean\")\n",
    "    ax4.bar(x + width/2, adv_probs[:num_classes].cpu(), width,\n",
    "            color=MALWARE_RED, label=\"adv\")\n",
    "    ax4.set_xlabel(\"Class\", color=WHITE)\n",
    "    ax4.set_ylabel(\"Probability\", color=WHITE)\n",
    "    legend = ax4.legend(facecolor=NODE_BLACK, edgecolor=HACKER_GREY)\n",
    "    for text in legend.get_texts():\n",
    "        text.set_color(WHITE)\n",
    "    ax4.set_title(\"Class probabilities\", color=HTB_GREEN, fontweight=\"bold\")\n",
    "    for text in ax4.get_xticklabels() + ax4.get_yticklabels():\n",
    "        text.set_color(HACKER_GREY)\n",
    "\n",
    "    # Add main title and display\n",
    "    fig.suptitle(title, color=HTB_GREEN, fontweight=\"bold\", fontsize=24, y=0.98)\n",
    "    fig.tight_layout(rect=(0, 0, 1, 0.93))\n",
    "    plt.show()\n",
    "\n",
    "def visualize_fgsm_attack(model: nn.Module,\n",
    "                         image: Tensor,\n",
    "                         label: Tensor,\n",
    "                         epsilon: float,\n",
    "                         num_classes: int = 10,\n",
    "                         targeted: bool = False,\n",
    "                         target_class: int | None = None) -> None:\n",
    "    \"\"\"Wrapper for visualize_attack using FGSM.\n",
    "\n",
    "    Args:\n",
    "        model: Classifier model\n",
    "        image: Single image tensor\n",
    "        label: True label\n",
    "        epsilon: Perturbation budget\n",
    "        num_classes: Classes to display\n",
    "        targeted: If True, targeted attack\n",
    "        target_class: Target class for targeted attack\n",
    "    \"\"\"\n",
    "    def _make_adv(m, xb, yb):\n",
    "        if targeted and target_class is None:\n",
    "            raise ValueError(\"target_class must be provided when targeted=True\")\n",
    "        y_used = yb if not targeted else torch.full_like(yb, target_class)\n",
    "        return fgsm_attack(m, xb, y_used, epsilon, targeted=targeted)\n",
    "\n",
    "    mode = \"Targeted\" if targeted else \"Untargeted\"\n",
    "    visualize_attack(model, image, label, _make_adv,\n",
    "                    title=f\"FGSM {mode}\",\n",
    "                    num_classes=num_classes,\n",
    "                    targeted=targeted,\n",
    "                    target_class=target_class)\n",
    "\n",
    "# Assume images, labels from test_loader (from Setup)\n",
    "# Assume epsilon from Core Implementation (epsilon=0.8)\n",
    "_ = visualize_fgsm_attack(model, images[0].detach().cpu(),\n",
    "                         labels[0].detach().cpu(), epsilon)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58454aa8-17b9-4c01-a698-b22993a71021",
   "metadata": {},
   "source": [
    "# Targetted FGSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b798e5c-89f4-4bfd-b39e-e49ccd640028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsilon=0.50 -> predicted 1\n",
      "epsilon=0.80 -> predicted 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5838/414685490.py:129: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(rect=(0, 0, 1, 0.93))\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eps_candidates = [0.5, 0.8, 1.0]\n",
    "success_image, success_label, success_eps = None, None, None\n",
    "\n",
    "model.eval()\n",
    "candidate, candidate_label = None, None\n",
    "\n",
    "for xb, yb in test_loader:\n",
    "    xb, yb = xb.to(device), yb.to(device)\n",
    "    match_indices = (yb == 1).nonzero(as_tuple=True)[0]\n",
    "    if len(match_indices) == 0:\n",
    "        continue\n",
    "\n",
    "    # Check predictions for all digit 1s in this batch\n",
    "    with torch.no_grad():\n",
    "        preds = model(xb[match_indices]).argmax(dim=1)\n",
    "        correct_mask = (preds == 1)\n",
    "        if correct_mask.any():\n",
    "            # Take first correctly classified digit 1\n",
    "            local_idx = correct_mask.nonzero(as_tuple=True)[0][0].item()\n",
    "            idx = match_indices[local_idx].item()\n",
    "            candidate = xb[idx]\n",
    "            candidate_label = yb[idx]\n",
    "            break\n",
    "\n",
    "if candidate is None:\n",
    "    raise RuntimeError(\"Could not find a correctly classified digit 1 in test set\")\n",
    "\n",
    "target_label = torch.tensor([7], device=device)\n",
    "\n",
    "for eps_try in eps_candidates:\n",
    "    x_adv = fgsm_attack(\n",
    "        model,\n",
    "        candidate.unsqueeze(0),\n",
    "        target_label,\n",
    "        epsilon=eps_try,\n",
    "        targeted=True,\n",
    "    )\n",
    "    with torch.no_grad():\n",
    "        pred = model(x_adv).argmax(dim=1).item()\n",
    "    print(f\"epsilon={eps_try:.2f} -> predicted {pred}\")\n",
    "\n",
    "    if pred == 7:\n",
    "        success_image = candidate\n",
    "        success_label = candidate_label\n",
    "        success_eps = eps_try\n",
    "        break\n",
    "\n",
    "if success_image is None:\n",
    "    raise RuntimeError(\"Targeted FGSM did not achieve 1 -> 7 within the tested epsilons.\")\n",
    "\n",
    "_ = visualize_fgsm_attack(\n",
    "    model,\n",
    "    success_image.detach().cpu(),\n",
    "    success_label.detach().cpu(),\n",
    "    success_eps,\n",
    "    targeted=True,\n",
    "    target_class=7,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2004dcaa-4722-430c-b41e-86454f2f1d0d",
   "metadata": {},
   "source": [
    "# I-FGSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60a4b12f-b610-439c-919a-ff706125d8e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def iterative_fgsm(model: nn.Module,\n",
    "                   images: Tensor,\n",
    "                   labels: Tensor,\n",
    "                   epsilon: float,\n",
    "                   num_iter: int,\n",
    "                   alpha: float | None = None,\n",
    "                   targeted: bool = False,\n",
    "                   random_start: bool = False) -> Tensor:\n",
    "    \"\"\"Iterative FGSM (Basic Iterative Method) with projection.\n",
    "\n",
    "    Args:\n",
    "        model: Target classifier in evaluation mode\n",
    "        images: Clean images (normalized)\n",
    "        labels: Ground-truth or target labels\n",
    "        epsilon: L_infinity budget (in normalized space)\n",
    "        num_iter: Number of iterations\n",
    "        alpha: Step size per iteration (defaults to epsilon/T)\n",
    "        targeted: If True, targeted attack\n",
    "        random_start: If True, initialize within the epsilon ball\n",
    "\n",
    "    Returns:\n",
    "        Tensor: Adversarial images (normalized)\n",
    "    \"\"\"\n",
    "    # Valid normalized range for MNIST\n",
    "    MNIST_NORM_MIN = (0.0 - 0.1307) / 0.3081\n",
    "    MNIST_NORM_MAX = (1.0 - 0.1307) / 0.3081\n",
    "\n",
    "    if alpha is None:\n",
    "        alpha = epsilon / max(num_iter, 1)\n",
    "    if random_start:\n",
    "        torch.manual_seed(1337)\n",
    "        delta = torch.empty_like(images).uniform_(-epsilon, epsilon)\n",
    "        x_adv = torch.clamp(images + delta, MNIST_NORM_MIN, MNIST_NORM_MAX)\n",
    "    else:\n",
    "        x_adv = images.clone()\n",
    "\n",
    "    for _ in range(num_iter):0.80\n",
    "        x_adv = x_adv.detach().requires_grad_(True)\n",
    "        logits = model(x_adv)\n",
    "        loss = F.cross_entropy(logits, labels)\n",
    "        model.zero_grad(set_to_none=True)\n",
    "        loss.backward()\n",
    "        step_dir = -1.0 if targeted else 1.0\n",
    "        x_adv = x_adv + step_dir * alpha * x_adv.grad.sign()\n",
    "        x_adv = torch.clamp(images + (x_adv - images).clamp(-epsilon, epsilon), MNIST_NORM_MIN, MNIST_NORM_MAX)\n",
    "\n",
    "    return x_adv.detach()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac658fda-76ce-4dba-a80a-8c9981ec50a5",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fac3e711-bfbb-42b0-97c2-9724663ea96c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I-FGSM flips (first batch): 100.00%\n"
     ]
    }
   ],
   "source": [
    "# Assume model, test_loader, device from FGSM Setup\n",
    "images, labels = next(iter(test_loader))\n",
    "images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "epsilon = 0.8\n",
    "num_iter = 10\n",
    "alpha = epsilon / num_iter  # alpha = 0.08\n",
    "\n",
    "with torch.no_grad():\n",
    "    clean_pred = model(images).argmax(dim=1)\n",
    "\n",
    "x_adv_ifgsm = iterative_fgsm(\n",
    "    model, images, labels,\n",
    "    epsilon=epsilon,\n",
    "    num_iter=num_iter,\n",
    "    alpha=alpha,\n",
    "    targeted=False,\n",
    "    random_start=True\n",
    ")\n",
    "\n",
    "with torch.no_grad():\n",
    "    adv_pred_ifgsm = model(x_adv_ifgsm).argmax(dim=1)\n",
    "\n",
    "originally_correct = clean_pred == labels\n",
    "flipped_ifgsm = (adv_pred_ifgsm != labels) & originally_correct\n",
    "print(\n",
    "    f\"I-FGSM flips (first batch): \"\n",
    "    f\"{(flipped_ifgsm.float().sum() / originally_correct.float().sum().clamp_min(1.0)).item():.2%}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b087eea-7d31-44f3-87a7-43dea6f686b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clean_accuracy: 1.0000\n",
      "adversarial_accuracy: 0.0000\n",
      "attack_success_rate: 1.0000\n",
      "avg_clean_confidence: 0.9854\n",
      "avg_adv_confidence: 0.0097\n",
      "avg_confidence_drop: 0.9757\n",
      "avg_l2_perturbation: 14.0366\n",
      "max_linf_perturbation: 0.8000\n"
     ]
    }
   ],
   "source": [
    "# Reuse evaluate_attack function from the Evaluation Metrics section\n",
    "metrics_ifgsm = evaluate_attack(model, images, x_adv_ifgsm, labels)\n",
    "for k, v in metrics_ifgsm.items():\n",
    "    print(f\"{k}: {v:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ba64fb-0d38-43eb-bb6f-99bb968e0201",
   "metadata": {},
   "source": [
    "# I-FGSM Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "553b92ee-1992-4ea3-99f9-aaed1bfafa08",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5838/414685490.py:129: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(rect=(0, 0, 1, 0.93))\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_ifgsm(model: nn.Module,\n",
    "                    image: Tensor,\n",
    "                    label: Tensor,\n",
    "                    epsilon: float,\n",
    "                    num_iter: int,\n",
    "                    targeted: bool = False,\n",
    "                    target_class: int | None = None) -> None:\n",
    "    \"\"\"Wrapper for visualize_attack using I-FGSM.\n",
    "\n",
    "    Args:\n",
    "        model: Classifier model\n",
    "        image: Single image tensor [C,H,W]\n",
    "        label: True label\n",
    "        epsilon: Perturbation budget\n",
    "        num_iter: Number of iterations\n",
    "        targeted: If True, targeted attack\n",
    "        target_class: Target class for targeted attacks\n",
    "    \"\"\"\n",
    "    alpha = epsilon / max(num_iter, 1)\n",
    "\n",
    "    def _make_adv(m, xb, yb):\n",
    "        y_used = yb if not targeted else torch.full_like(yb, target_class)\n",
    "        return iterative_fgsm(\n",
    "            m, xb, y_used,\n",
    "            epsilon, num_iter, alpha,\n",
    "            targeted=targeted,\n",
    "            random_start=True\n",
    "        )\n",
    "\n",
    "    mode = \"Targeted\" if targeted else \"Untargeted\"\n",
    "    visualize_attack(\n",
    "        model, image, label, _make_adv,\n",
    "        title=f\"I-FGSM {mode}\",\n",
    "        targeted=targeted,\n",
    "        target_class=target_class\n",
    "    )\n",
    "\n",
    "# Visualize first sample from test batch\n",
    "_ = visualize_ifgsm(\n",
    "    model,\n",
    "    images[0].detach().cpu(),\n",
    "    labels[0].detach().cpu(),\n",
    "    epsilon,\n",
    "    num_iter,\n",
    "    targeted=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d519e953-f706-4809-afe1-0b16fe98eccc",
   "metadata": {},
   "source": [
    "# I-FGSM Targetted Misclassification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "52efca80-8c8c-4f9e-a4bc-734bca050ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epsilon=0.50 -> predicted 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5838/414685490.py:129: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(rect=(0, 0, 1, 0.93))\n"
     ]
    },
    {
     "data": {
      "image/png": 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3/yI42/LvGyp6RzuOtrzitP716wo9/efc6MOLrlun/NK09np/l+Yc2aW3n819MB7Jtm1cHtS46bkP6Ud/tEV/vzqgcdPjmn143z7NK+kZpbgqoGTcJV8gq8oZCe1zfIdWPJevYz7RNLC/2wnxd0Y7k/aOqubUdklSLOLWy/cXS5IiLV7dvnCSzv3uJn3r37lvVNe9HtJfvzlRlbNiOuYTzXrtkUK99WSBpNyXF5K47+N7wPkFgd4PwvdH4opkpceiCZ2eF1B+TwB5R8/ovzyXBty/7YuNXbo3EtcRQZ9uGVcwqO27uuJaVJqnPLdLH84P9IaP5+b3PeZtnbm29/d7ej9MP9ad0FeaI2pIZXRY0KvFVYXKd7v04/J8fWBT+6DH8Uq6oL5Dz8SSGu91qymd0ft6RvusSaZ1el2HWtIZjfO6dYDfq4nefqOBE2l9pL5DrydSak1n5XJJ+/q9uquqUHluly4tCg0ZPpZ73Ppte1S/bIsqJalwG1NW61MZXdTQqVfjKbVkMkplpVk+j/5aVaCJXo8uKAjoe63d6rA8RbHS49IPS/uCx5+3desPHTFFM7nRkCWebb+eTbZjS7gsSSdvatfSeEqlHpdm+zw6IeTvDY+21HVlsjphY5vWpTKq8Li1t9+jmsDAy0InC1xI256CfHFhUOGe5+qOrrhaLe/zCo9bX2js0n39XidBt0sXFQR0Y0dUK5O5PbGj27YlfA+4pJqAVzWBfB0a9OmSxtyHr8n9jvkJXo8+Vdj3oW2236OrSvM01+cxHglX2dNuhcelD+X59YGwTx9v6NSj0eHfUz8Y9unontfnplRuJLIkvZVM68tNES0qDevtqbkvYx/vTuhbzREdH/LpnPyAft0e1es9oZpXko1468fbGMW8xUhf0yUet+7piqu2pVsdmYwqPW5N87r1mZ4Rg8lsVp/e3KUnowmdlR/Qz8sHfyFpcq48s75j0EjcrZ/j/r/fnm0tIHNpv/D0N0OcM4GxJODN6MOHtGuvCX3n05dX565vfnBOnaqKUkqmpc/dOkmPvFGg/EBG13y4TmfUdOjsgzp010udeuTNgdc9JXlp3fNKoWrvq1JH1K3KwpSuuH3igAVvtox23BmGerytvb4hqBN/nJtm/vuLNuiQ6VFNLEnpshOadfUDuRmP1z5UoTVNfm1q86kr7lZJOK3vzG/QRw9vU1VRSuce3K4/PFWmgxbN0YJDW3tHbm5rwZX+5h/Q0Rs8rmv26eKbJmt1k19fPKlJV57UpPxgRr/4yCa9/5pZg/42mnTpvJ/M0Nomn27+1HodPbtb5flpnbh3l+793/DnLZcrqwN6gsA3Nw3+jP/rx8t01OyITprXpUuPa9bMcQkd2FN/9QPj9Mqa4RdV25aKgr7noL3fjMSOfgM3fB6pNC+lzZ1brpNcWrYxqKNnd6tmalS5mwfx+WpXIHzchZwe0ture+eFPD1565aVo3IvrJIJg0dO+IIZTa/JDcPOZKT7rq1SrNOjaIdX/7mlXOd8p85hj/pk0tI9V09QtNOjdW1e1a8MaPK8mHzBrArKUups9imddMvnz+r8H2xU1cy4gvlpubf6oqRyRtx6+DitplsFZbkL5qZ1ft31vQm9wc2aV8Na8+q2T3Im29G8vi9IPnJBiyqmJtS4xq9NK4L692/63iSa+wXOx3+6SWuWhNS4JqANb4b0z5/v/gsMtTd49ejvKiS5tPa1sF74e4lO+HQuqJtzRF/4OBKP/q5Ce72/S8G8jA78ULsO/NDgQCTdM+owHvHo8RvL9cHLcxcen/jF+iHbTCe3/Wrb0Xam1UR08XXr5fVnlUq4dPtXJ6ljc19AsPzpAl190hwVVaaUjLnU3e6Vy5XVZX9arWTUpXt/VKWKaXGd9a263tfymlfD+vvV49W4JjDkY2LsO7/f6J0ti03c25ULH6Xc1Nkt4eOhQV/vfeFejad0e8/PH40m9c/uhM7ZaiRQJCvdE4nrwoKg9vJ7tZ/fo9cSaX24p649ndF9kVwbp/Zb8OaEsF9LwoO/EKsJeFXqdqllqw/0N3TE9O+esGNVT5izJpnRPL803uvWV4tDeiuZ1spkWk/FkgMCgc3pjM7PD+hbJWFN93kUcql3Sq4klXncqnC71LjVY76bTOu7Ld29I8vathFitWaymuJ168riAs3yuZXvcg14DK/LpVk+j/5neaTQiSG/Qj3P3zPRpH7Y2vcNvZNptybbsaZfQPHFopBejCe1MpnW0nha34v1LR6ypmfEbNglfbUkrKXxlFYl01oST+mxbQRYI+GX9MmeUYjpbFa/3QXTQl+MJXun7vd/nbhdLh0b8mtlcuQhTXcmqx+1duvR7oRWJdPyu1xakB/QVaVhuV0unZMf0E0dMb0UT8m31Ui7v3bG9M3mbs3xe/T3nqB9QUFQv2mP6a3ktr8Iey2R0uWNXXoullRDOqPJXrd+WJqn48J++VwuXV2Wp0eHWSH9tLBfv+kZEduezuhjDZ0DRkf+pSuuv3bFNcnrVls6q85sViGX9H9leVqXTOva1m4dFPDqh2V52tfvUTIrPRFN6mvNXWpI77qJ7CN9TXdkMvpiU1fvaPJ3Uxl9vN8XQP/qTujBnjD2z51xXZAf0MH9gnxpx86Vtszze/T+fl/4/OM9cB9VvDctOKxt0FRoSbr3f4V6cXVYAW9Gx++V+9LG55Fu/uSGIds5fq+uQeFjR9StL/51oroTudDp3Ua7M4WcPN5376lSY2cuYrn2oXH62+fXSpKOndvVGz62Rjz65mmbdcCUqErz0r2rOW8xp2rb9xPenpP37ez9/98+UdY7FfmH/xynC45oVVl+WntNiGtaeUJrmgaez371aHnvlOd/vFqoo3umdE8p3fY5pjQvrUDPdjR3DfU8uHTZbRP1xNdWaWJJSqf09PHh1wt0wxPOvgzcnv5vu1u9BSu7VcKy5T6PAW9WpXlptUSIxXYF9vIuFGnzKN7t7h39WDopobqtArhQYUqhwr4PEK11g1fj3fims9AuVNgXlsW63Ip19p0IWjdtf5XfoXQ2eXvvXShJiX7fMHh7Rhi+/+NNOv0rDdtsxxewP4GnoKzvArTh3YDxiDGT7XjjP4V68tYyHXFei2Ye3K2ZB/d90NvwZlC3XDFFHY0+PXdHiSbtHdX+J3do3rGdmnds35vDO8/n6Y9fmNw7xfZHp8zR7iY3pbhvP7f1O87yS/uej5Fs2+Z3g/r1x6frg5dt1oyDI/J4pfp3AmpcE+i9T2nrpr430Ed/P07tm306+qMtGjcjru52j955Pk/j58Q0oTreU7/918FI29n3pHZ95Acb5QtmFYu4ddv/m6y3nxsqfHUNWHX78PNaNXX/qP5+9XhFWr269OaVqpiW0H3X5sLpMxbW66Lr1umnZ81SJs23dLubwwNezfDlXuOt6Yzi2az28XtUl84omc3K53LpyKBXk71urU9lVNpvdN/GrUa/bBhmNMyfOnLhoyR9OD+gTFdc83qm5v2tK94bPFQ4nKdU4hn8gfq1IUK7b7dEVOZx6fCgTx/vdx+yRDarP3TEVNuzgMXvxxXog0N8eO8v6HZJWz3m6/GU43v2XV0a1qeLtn1/ouAuePmM8/Qf8WkedJpsxy0dMe3n9+qsfL9Ozsv9t8WT0YQ+0dCpSDY3ymyGz6MTQj6dmx/oncqazmZ1byShzzf2TbHd0cV8zs0P9I7Ue6g7odXDHLM709avi/7/Lu/3ehrJtjVnsvpZv2npkWxWN3TEdHzY13s/1UMCXr0UT6l5q+m7v+uIqSub1f/iKf0nmtSHep6f/QLe7YaP/9pqteSVyYyubIro9Sm5Nmb6PEMGX58pDOr7pWF5ehb6+UhDh14bYmpwVhowuu6rxWFN83m0oL5DfpdLt1cWKOxy6f81RTTL59EXikMqdBfo7CEWF7JlpK/plcnMgNtYSFKZu+91ufXxsj6V0cFbtbEj58qhPBtL7fBr69LCvn3x+w7z6fvAaOiKubWiPqC/vVSkW3pWQi7JS8vnIDMszR/8Hrpyc6A3CByJrQMq73aacvJ4G1r7runXt/T9f3lP/w+c2q17rljTe5/CoYR8O/YlRv/RgOv79Sedcamuzaey/HRv3dbh49sNfV9q99/WwA72SZJau7267bkSff3Uxt6f/ebxMu3IqMPGTq9mVeaC0aJ+t8sqDPa91yVSLrVGuIXVWED4uAtlM67exTEk6ZCz2vTGE4UDag49u633/7taPNr41uALrUTM2Uk22uFROpW7J10wP6NAXlrxnheekymvQ0lvfT+7Ic5D/Uek3XtNlV64u0SpuFsf/9k67Xti5+A/sKSzue/wHjc9Lpcrq2zW+cnNdDv+8dMqPXTdOI2fHVfpxIRmHNytoz7Sokl7x3Ti5xr196snKJ1ya/G3JunuqzOaMDumkkkJ7f3+LtWc2q7Zh0d01Eda9MTNuftO7I7Trouqkuo/dL14Ql//u1r6no+Rblv9O0Hd+sUpA2o+ek3fN6TvPDfwPmYv3Vuil+7tm2oRLk7p6/98p6c/Hm1a4SzIN23n6AubdfqX6+X2SO2bvbr58inatHz7N2ourEjqlCsbtObVkJ7/W4nGzYirYlpCdW8H9Myfc98KHnpmq8bPiatiWlwNq+yOHsbOt6Cg76KuxOPW4xOLB9W4XS6dnx/QT9qiAz7ITtzqqnjSMFfJSxIpLYuntE/Aq7PzAupf9afOvm/TG/uFI99vieg6g2l73dnBJ/8NqYxOr+tQuduluX6Ppnk9urAgoIOCPn2+KKT7Iwm9k0z3Bo+xTFbn1HfolXhKaUkrppSodBsf8qNDPOZw+t8b7hMNnXqkO6Gk+hbk2VU299vHc/3mj2uyHUlJlzd1aWGztLffqyletz4Q9uuc/ICOCfn1qcKgrmuPqSOT1QUNnSp0u7S3z6MpPrfOyQvo+J7ah7sTvSNyd3Rq8ueK+i+GsWumhW79uuj/76Z+r6eRbJtb2m7Is+X3q1MZtaYzKhnimO5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      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find one sample of '1'\n",
    "one_img, one_lbl = None, None\n",
    "for xb, yb in test_loader:\n",
    "    m = (yb == 1)\n",
    "    if m.any():\n",
    "        j = m.nonzero(as_tuple=True)[0][0].item()\n",
    "        one_img = xb[j].to(device)\n",
    "        one_lbl = yb[j].to(device)\n",
    "        break\n",
    "\n",
    "# Try increasing epsilon values until successful\n",
    "for eps_try in [0.5, 0.8, 1.0]:\n",
    "    x_adv = iterative_fgsm(\n",
    "        model,\n",
    "        one_img.unsqueeze(0),\n",
    "        torch.tensor(7, device=device).unsqueeze(0),  # target label\n",
    "        epsilon=eps_try,\n",
    "        num_iter=num_iter,\n",
    "        alpha=eps_try / max(num_iter, 1),\n",
    "        targeted=True,\n",
    "        random_start=True,\n",
    "    )\n",
    "    with torch.no_grad():\n",
    "        pred = model(x_adv).argmax(dim=1).item()\n",
    "    print(f\"epsilon={eps_try:.2f} -> predicted {pred}\")\n",
    "\n",
    "    if pred == 7:\n",
    "        _ = visualize_ifgsm(\n",
    "            model,\n",
    "            one_img.detach().cpu(),\n",
    "            one_lbl.detach().cpu(),\n",
    "            eps_try,\n",
    "            num_iter,\n",
    "            targeted=True,\n",
    "            target_class=7,\n",
    "        )\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "321c1bea-8d8d-451c-a9f2-0aa38ac22de1",
   "metadata": {},
   "source": [
    "# Comparison I-FGSG vs FGSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "831947b7-3c82-4599-864d-d936c2520e46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FGSM success rate: 60.2%\n",
      "I-FGSM success rate: 96.1%\n",
      "Improvement: 59.7%\n"
     ]
    }
   ],
   "source": [
    "# Compare FGSM (one-step) and I-FGSM on the same batch\n",
    "# Run both attacks with same epsilon\n",
    "epsilon = 0.7\n",
    "x_adv_fgsm = fgsm_attack(model, images, labels, epsilon)\n",
    "x_adv_ifgsm = iterative_fgsm(\n",
    "    model, images, labels,\n",
    "    epsilon, num_iter=10,\n",
    "    random_start=True\n",
    ")\n",
    "\n",
    "# Compare success rates\n",
    "with torch.no_grad():\n",
    "    fgsm_pred = model(x_adv_fgsm).argmax(dim=1)\n",
    "    ifgsm_pred = model(x_adv_ifgsm).argmax(dim=1)\n",
    "\n",
    "orig_correct = clean_pred == labels\n",
    "fgsm_success = (\n",
    "    ((fgsm_pred != labels) & orig_correct).float().sum()\n",
    "    / orig_correct.float().sum().clamp_min(1.0)\n",
    ")\n",
    "ifgsm_success = (\n",
    "    ((ifgsm_pred != labels) & orig_correct).float().sum()\n",
    "    / orig_correct.float().sum().clamp_min(1.0)\n",
    ")\n",
    "\n",
    "print(f\"FGSM success rate: {fgsm_success:.1%}\")\n",
    "print(f\"I-FGSM success rate: {ifgsm_success:.1%}\")\n",
    "print(f\"Improvement: {(ifgsm_success - fgsm_success) / fgsm_success:.1%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a03f665-bd8f-43c8-9713-8552bccc21d6",
   "metadata": {},
   "source": [
    "# Deepfool Setup "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "194c8de1-72c9-470b-ba92-246cc15332a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from htb_ai_library import (\n",
    "    set_reproducibility,\n",
    "    MNISTClassifierWithDropout,\n",
    "    get_mnist_loaders,\n",
    "    train_model,\n",
    "    evaluate_accuracy,\n",
    "    save_model,\n",
    "    load_model,\n",
    "    analyze_model_confidence,\n",
    "    HTB_GREEN, NODE_BLACK, HACKER_GREY, WHITE,\n",
    "    AZURE, NUGGET_YELLOW, MALWARE_RED, VIVID_PURPLE, AQUAMARINE\n",
    ")\n",
    "\n",
    "set_reproducibility(1337)\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3756214c-dc3d-48fb-b27f-f3166cc16cc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model parameters: 1,625,866\n"
     ]
    }
   ],
   "source": [
    "# MNISTClassifierWithDropout is imported from htb_ai_library\n",
    "# The architecture internally defines:\n",
    "# - Conv1: 1->32 channels, 3x3 kernel, ReLU, 2x2 pooling, 25% dropout\n",
    "# - Conv2: 32->64 channels, 3x3 kernel, ReLU, 2x2 pooling, 25% dropout\n",
    "# - FC1: 3136->128, ReLU, 50% dropout\n",
    "# - FC2: 128->10 (logits)\n",
    "\n",
    "model = MNISTClassifierWithDropout().to(device)\n",
    "print(f\"Model parameters: {sum(p.numel() for p in model.parameters()):,}\")\n",
    "\n",
    "model_path = 'output/mnist_model.pth'\n",
    "os.makedirs('output', exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "136fb376-4076-4542-9e6d-5d93cca82958",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found cached model at output/mnist_model.pth\n",
      "Model loaded from output/mnist_model.pth\n",
      "Cached model accuracy: 99.20%\n"
     ]
    }
   ],
   "source": [
    "# Try loading cached model\n",
    "if os.path.exists(model_path):\n",
    "    print(f\"Found cached model at {model_path}\")\n",
    "    model_data = load_model(model_path)\n",
    "    model = model_data['model'].to(device)\n",
    "    model.eval()\n",
    "\n",
    "    # Validate cached model\n",
    "    _, test_loader = get_mnist_loaders(batch_size=100, normalize=True)\n",
    "    accuracy = evaluate_accuracy(model, test_loader, device)\n",
    "    print(f\"Cached model accuracy: {accuracy:.2f}%\")\n",
    "\n",
    "    if accuracy < 90.0:\n",
    "        print(\"Accuracy below threshold, retraining required\")\n",
    "        model = None\n",
    "else:\n",
    "    model = None\n",
    "\n",
    "# Train if needed\n",
    "if model is None:\n",
    "    print(\"Training new model...\")\n",
    "    train_loader, test_loader = get_mnist_loaders(batch_size=64, normalize=True)\n",
    "    model = MNISTClassifierWithDropout().to(device)\n",
    "\n",
    "    model = train_model(\n",
    "        model, train_loader, test_loader,\n",
    "        epochs=5, device=device\n",
    "    )\n",
    "    \n",
    "    # Evaluate and cache\n",
    "    accuracy = evaluate_accuracy(model, test_loader, device)\n",
    "    print(f\"Test Accuracy: {accuracy:.2f}%\")\n",
    "\n",
    "    save_model({\n",
    "        'model': model,\n",
    "        'architecture': 'MNISTClassifierWithDropout',\n",
    "        'accuracy': accuracy,\n",
    "        'training_config': {\n",
    "            'epochs': 5,\n",
    "            'batch_size': 64,\n",
    "            'device': str(device)\n",
    "        }\n",
    "    }, model_path)\n",
    "\n",
    "# Analyze confidence distribution\n",
    "_, test_loader = get_mnist_loaders(batch_size=100, normalize=True)\n",
    "stats = analyze_model_confidence(model, test_loader, device=device, num_samples=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc6a60f-03ec-4fd4-83c9-22c49f1e2d3c",
   "metadata": {},
   "source": [
    "# DeepFool Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4d15477a-6873-4b4f-ae95-22f97a2d2b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Tuple\n",
    "\n",
    "def deepfool(image: torch.Tensor,\n",
    "             net: nn.Module,\n",
    "             num_classes: int = 10,\n",
    "             overshoot: float = 0.02,\n",
    "             max_iter: int = 50,\n",
    "             device: str = 'cuda') -> Tuple[torch.Tensor, int, int, int, torch.Tensor]:\n",
    "    \"\"\"\n",
    "    Generate minimal adversarial perturbation using DeepFool algorithm.\n",
    "\n",
    "    Args:\n",
    "        image (torch.Tensor): Input image tensor of shape (1, C, H, W)\n",
    "        net (nn.Module): Target neural network in evaluation mode\n",
    "        num_classes (int): Number of top-scoring classes to consider (default: 10)\n",
    "        overshoot (float): Overshoot parameter for boundary crossing (default: 0.02)\n",
    "        max_iter (int): Maximum iterations before terminating (default: 50)lon=0.5\n",
    "        device (str): Computation device ('cuda' or 'cpu')\n",
    "\n",
    "    Returns:\n",
    "        Tuple containing:\n",
    "            - r_tot (torch.Tensor): Total accumulated perturbation\n",
    "            - loop_i (int): Number of iterations performed\n",
    "            - label (int): Original predicted class\n",
    "            - k_i (int): Final adversarial class\n",
    "            - pert_image (torch.Tensor): Final perturbed image\n",
    "    \"\"\"\n",
    "    image = image.to(device)\n",
    "    net = net.to(device)\n",
    "\n",
    "    # Original prediction and class ordering (descending score)\n",
    "    f_image = net(image).data.cpu().numpy().flatten()\n",
    "    I = f_image.argsort()[::-1]\n",
    "    label = I[0]\n",
    "\n",
    "    # Working tensors and accumulators\n",
    "    input_shape = image.shape\n",
    "    pert_image = image.clone()\n",
    "    r_tot = torch.zeros(input_shape).to(device)\n",
    "    loop_i = 0\n",
    "\n",
    "    # Iterate until a successful perturbation is found or the limit is reached\n",
    "    while loop_i < max_iter:\n",
    "        x = pert_image.clone().requires_grad_(True)\n",
    "        fs = net(x)\n",
    "        # Current top prediction at x\n",
    "        k_i = fs.data.cpu().numpy().flatten().argsort()[::-1][0]\n",
    "\n",
    "        # Stop when the prediction changes\n",
    "        if k_i != label:\n",
    "            break\n",
    "\n",
    "        # Initialize the best candidate step for this iteration\n",
    "        pert = float('inf')\n",
    "        w = Nonelon=0.5\n",
    "\n",
    "        # Search minimal step among candidate classes\n",
    "        for k in range(1, num_classes):\n",
    "            if I[k] == label:\n",
    "                continue\n",
    "\n",
    "            # Compute gradient for candidate class\n",
    "            if x.grad is not None:\n",
    "                x.grad.zero_()\n",
    "            fs[0, I[k]].backward(retain_graph=True)\n",
    "            grad_k = x.grad.data.clone()\n",
    "\n",
    "            # Compute gradient for original class\n",
    "            if x.grad is not None:\n",
    "                x.grad.zero_()\n",
    "            fs[0, label].backward(retain_graph=True)\n",
    "            grad_label = x.grad.data.clone()\n",
    "\n",
    "            # Direction and distance under linearization\n",
    "            w_k = grad_k - grad_label\n",
    "            f_k = (fs[0, I[k]] - fs[0, label]).data.cpu().numpy()\n",
    "            pert_k = abs(f_k) / (torch.norm(w_k.flatten()) + 1e-10)\n",
    "\n",
    "            if pert_k < pert:\n",
    "                pert = pert_k\n",
    "                w = w_k\n",
    "\n",
    "        # Minimal step for the selected direction\n",
    "        r_i = (pert + 1e-4) * w / (torch.norm(w.flatten()) + 1e-10)\n",
    "        r_tot = r_tot + r_i\n",
    "\n",
    "        # Apply with overshoot to ensure crossing\n",
    "        pert_image = image + (1 + overshoot) * r_tot\n",
    "        loop_i += 1\n",
    "\n",
    "    return r_tot, loop_i, label, k_i, pert_image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1bfb018-2fa9-439e-b589-85a7862114ea",
   "metadata": {},
   "source": [
    "# Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1e0b054f-4ff0-4b88-b811-642ab8ca6ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded from output/mnist_model.pth\n",
      "True label: 7\n",
      "Original: class 7 (confidence: 1.000)\n",
      "Attack: 7 → 3 in 4 iterations\n",
      "\n",
      "=== Attack Results ===\n",
      "L2 norm: 7.5054\n",
      "L∞ norm: 1.4652\n",
      "Relative perturbation: 31.57%\n",
      "Original confidence: 1.000\n",
      "Adversarial confidence: 0.496\n"
     ]
    }
   ],
   "source": [
    "# Load trained modellon=0.5\n",
    "model_path = 'output/mnist_model.pth'\n",
    "if os.path.exists(model_path):\n",
    "    model_data = load_model(model_path)\n",
    "    model = model_data['model'].to(device)\n",
    "    model.eval()\n",
    "else:\n",
    "    raise FileNotFoundError(\"Model not found.\")\n",
    "\n",
    "# Get single test sample\n",
    "_, test_loader = get_mnist_loaders(batch_size=1, normalize=True)\n",
    "dataiter = iter(test_loader)\n",
    "image, true_label = next(dataiter)\n",
    "image = image.to(device)\n",
    "\n",
    "print(f\"True label: {true_label.item()}\")\n",
    "\n",
    "# Baseline classification\n",
    "with torch.no_grad():\n",
    "    original_output = model(image)\n",
    "    original_pred = original_output.argmax(dim=1).item()\n",
    "    original_confidence = F.softmax(original_output, dim=1).max().item()\n",
    "\n",
    "print(f\"Original: class {original_pred} (confidence: {original_confidence:.3f})\")\n",
    "\n",
    "# Execute DeepFool attack\n",
    "r_total, iterations, orig_label, pert_label, pert_image = deepfool(\n",
    "    image, model, num_classes=10, overshoot=0.02, max_iter=50, device=device\n",
    ")\n",
    "\n",
    "print(f\"Attack: {orig_label} → {pert_label} in {iterations} iterations\")\n",
    "\n",
    "# Compute perturbation norms\n",
    "perturbation_norm_l2 = torch.norm(r_total).item()\n",
    "perturbation_norm_linf = torch.abs(r_total).max().item()\n",
    "relative_perturbation = perturbation_norm_l2 / torch.norm(image).item()\n",
    "\n",
    "# Evaluate adversarial confidence\n",
    "with torch.no_grad():\n",
    "    adv_output = model(pert_image)\n",
    "    adv_confidence = F.softmax(adv_output, dim=1).max().item()\n",
    "lon=0.5\n",
    "# Display results\n",
    "print(f\"\\n=== Attack Results ===\")\n",
    "print(f\"L2 norm: {perturbation_norm_l2:.4f}\")\n",
    "print(f\"L∞ norm: {perturbation_norm_linf:.4f}\")\n",
    "print(f\"Relative perturbation: {relative_perturbation:.2%}\")\n",
    "print(f\"Original confidence: {original_confidence:.3f}\")\n",
    "print(f\"Adversarial confidence: {adv_confidence:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8f87dc5f-0e82-46a3-a085-fb782420b548",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepare images for visualization\n",
    "original_img = mnist_denormalize(image.squeeze()).cpu().numpy()\n",
    "adversarial_img = mnist_denormalize(pert_image.squeeze()).cpu().numpy()\n",
    "perturbation = r_total.cpu().squeeze().numpy()\n",
    "\n",
    "# Normalize perturbation for visibility (amplify minimal changes)\n",
    "pert_display = perturbation - perturbation.min()\n",
    "if pert_display.max() > 0:\n",
    "    pert_display = pert_display / pert_display.max()\n",
    "\n",
    "# Create four-panel visualization\n",
    "fig, axes = plt.subplots(1, 4, figsize=(15, 5))\n",
    "fig.patch.set_facecolor(NODE_BLACK)\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_facecolor(NODE_BLACK)\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor(HACKER_GREY)\n",
    "\n",
    "# Panel 1: Original clean image\n",
    "axes[0].imshow(original_img, cmap='gray', vmin=0, vmax=1)\n",
    "axes[0].set_title(f\"Original\\nClass: {original_pred}\",\n",
    "                  color=HTB_GREEN, fontweight='bold')\n",
    "axes[0].axis('off')\n",
    "\n",
    "# Panel 2: Amplified perturbation pattern\n",
    "axes[1].imshow(pert_display, cmap='inferno')\n",
    "axes[1].set_title(\"Perturbation\\n(amplified)\",\n",
    "                  color=NUGGET_YELLOW, fontweight='bold')\n",
    "axes[1].axis('off')\n",
    "\n",
    "# Panel 3: Perturbation magnitude heatmap\n",
    "im = axes[2].imshow(np.abs(perturbation), cmap='viridis')\n",
    "axes[2].set_title(f\"Magnitude\\nL2: {perturbation_norm_l2:.4f}\",\n",
    "                  color=AZURE, fontweight='bold')\n",
    "axes[2].axis('off')\n",
    "plt.colorbar(im, ax=axes[2], fraction=0.046, pad=0.04)\n",
    "\n",
    "# Panel 4: Adversarial result\n",
    "title_color = HTB_GREEN if pert_label != original_pred else MALWARE_RED\n",
    "axes[3].imshow(adversarial_img, cmap='gray', vmin=0, vmax=1)\n",
    "axes[3].set_title(f\"Adversarial\\nClass: {pert_label}\",\n",
    "                  color=title_color, fontweight='bold')\n",
    "axes[3].axis('off')\n",
    "\n",
    "# Summary metrics\n",
    "metrics_text = (\n",
    "    f\"Iterations: {iterations}  |  \"\n",
    "    f\"Relative pert: {relative_perturbation:.2%}  |  \"\n",
    "    f\"Confidence: {original_confidence:.3f} → {adv_confidence:.3f}\"\n",
    ")\n",
    "fig.text(0.5, 0.02, metrics_text, ha='center', fontsize=10, color=WHITE)\n",
    "\n",
    "plt.suptitle(\"DeepFool Attack Visualization\", fontsize=14,\n",
    "             color=HTB_GREEN, fontweight='bold', y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ccc2aa2-0b43-41f8-b6b6-90036d0f2d56",
   "metadata": {},
   "source": [
    "# Batch Attack Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "78691b1e-aae7-4a63-9ec8-ecc135c6351d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generating 20 adversarial examples using DeepFool...\n",
      "Test loader ready with 10000 samples\n",
      "Will process first 20 samples\n",
      "Starting batch attack generation...\n",
      "  Example 1: True=7, Orig=7, Adv=3, Iter=4, L2=7.5054\n",
      "  Example 2: True=2, Orig=2, Adv=6, Iter=3, L2=6.6667\n",
      "  Example 3: True=1, Orig=1, Adv=4, Iter=1, L2=4.7865\n",
      "  Example 4: True=0, Orig=0, Adv=6, Iter=3, L2=5.5675\n",
      "  Example 5: True=4, Orig=4, Adv=9, Iter=3, L2=3.7576\n",
      "  Example 6: True=1, Orig=1, Adv=7, Iter=1, L2=4.9832\n",
      "  Example 7: True=4, Orig=4, Adv=8, Iter=2, L2=1.6978\n",
      "  Example 8: True=9, Orig=9, Adv=4, Iter=1, L2=3.4954\n",
      "  Example 9: True=5, Orig=5, Adv=6, Iter=1, L2=0.5942\n",
      "  Example 10: True=9, Orig=9, Adv=4, Iter=2, L2=4.1109\n",
      "  Example 11: True=0, Orig=0, Adv=2, Iter=4, L2=6.8036\n",
      "  Example 12: True=6, Orig=6, Adv=0, Iter=4, L2=5.3710\n",
      "  Example 13: True=9, Orig=9, Adv=4, Iter=3, L2=3.7684\n",
      "  Example 14: True=0, Orig=0, Adv=6, Iter=4, L2=8.1619\n",
      "  Example 15: True=1, Orig=1, Adv=5, Iter=3, L2=4.2609\n",
      "  Example 16: True=5, Orig=5, Adv=3, Iter=3, L2=3.2559\n",
      "  Example 17: True=9, Orig=9, Adv=4, Iter=3, L2=4.4424\n",
      "  Example 18: True=7, Orig=7, Adv=3, Iter=3, L2=7.5655\n",
      "  Example 19: True=3, Orig=3, Adv=5, Iter=2, L2=1.7059\n",
      "  Example 20: True=4, Orig=4, Adv=9, Iter=4, L2=6.0066\n",
      "\n",
      "Attack Success Rate: 20/20 (100.0%)\n",
      "Average L2 norm: 4.7254\n",
      "Average iterations: 2.7\n",
      "\n",
      "Generating attack grid visualization...\n",
      "Grid visualization saved to output/deepfool_examples.png\n"
     ]
    }
   ],
   "source": [
    "num_examples = 20\n",
    "print(f\"\\nGenerating {num_examples} adversarial examples using DeepFool...\")\n",
    "\n",
    "_, test_loader = get_mnist_loaders(batch_size=1, normalize=True)\n",
    "model.eval()\n",
    "\n",
    "results = []\n",
    "success_count = 0\n",
    "\n",
    "print(f\"Test loader ready with {len(test_loader.dataset)} samples\")\n",
    "print(f\"Will process first {num_examples} samples\")\n",
    "print(\"Starting batch attack generation...\")\n",
    "\n",
    "for idx, (data, target) in enumerate(test_loader):\n",
    "    if idx >= num_examples:\n",
    "        break\n",
    "\n",
    "    data = data.to(device)\n",
    "\n",
    "    # Execute DeepFool attack\n",
    "    r, iterations, orig_label, adv_label, pert_image = deepfool(\n",
    "        data, model, num_classes=10, overshoot=0.02, max_iter=50, device=device\n",
    "    )\n",
    "\n",
    "    # Track success and store metrics\n",
    "    success = (orig_label != adv_label)\n",
    "    if success:\n",
    "        success_count += 1\n",
    "\n",
    "    results.append({\n",
    "        'original_image': data.cpu(),\n",
    "        'perturbation': r.cpu(),\n",
    "        'perturbed_image': pert_image.cpu(),\n",
    "        'original_label': orig_label,\n",
    "        'adversarial_label': adv_label,\n",
    "        'iterations': iterations,\n",
    "        'true_label': target.item(),\n",
    "        'l2_norm': torch.norm(r.cpu()).item(),\n",
    "        'success': success\n",
    "    })\n",
    "\n",
    "    # Progress feedback\n",
    "    print(f\"  Example {idx+1}: True={target.item()}, Orig={orig_label}, \"\n",
    "          f\"Adv={adv_label}, Iter={iterations}, L2={torch.norm(r.cpu()).item():.4f}\")\n",
    "\n",
    "print(f\"\\nAttack Success Rate: {success_count}/{num_examples} \"\n",
    "      f\"({100*success_count/num_examples:.1f}%)\")\n",
    "print(f\"Average L2 norm: {np.mean([r['l2_norm'] for r in results]):.4f}\")\n",
    "print(f\"Average iterations: {np.mean([r['iterations'] for r in results]):.1f}\")\n",
    "\n",
    "def visualize_attack_grid(results, save_dir='output'):\n",
    "    \"\"\"\n",
    "    Create grid visualization showing original and adversarial images side-by-side.\n",
    "\n",
    "    Args:\n",
    "        results (list): Attack results from batch generation\n",
    "        save_dir (str): Directory to save visualization\n",
    "    \"\"\"\n",
    "    print(\"\\nGenerating attack grid visualization...\")\n",
    "\n",
    "    num_examples = min(10, len(results))\n",
    "    fig, axes = plt.subplots(4, 5, figsize=(15, 12))\n",
    "    fig.patch.set_facecolor(NODE_BLACK)\n",
    "\n",
    "    for ax in axes.flatten():\n",
    "        ax.set_facecolor(NODE_BLACK)\n",
    "        for spine in ax.spines.values():\n",
    "            spine.set_edgecolor(HACKER_GREY)\n",
    "\n",
    "    for idx in range(num_examples):\n",
    "        row = idx // 5\n",
    "        col = idx % 5\n",
    "\n",
    "        # Original image (top row for this column)\n",
    "        ax_original = axes[row * 2, col]\n",
    "        img = mnist_denormalize(results[idx]['original_image'].squeeze()).numpy()\n",
    "        ax_original.imshow(img, cmap='gray', vmin=0, vmax=1)\n",
    "        ax_original.set_title(f\"Original: {results[idx]['original_label']}\",\n",
    "                            color=HTB_GREEN, fontsize=10)\n",
    "        ax_original.axis('off')\n",
    "\n",
    "        # Adversarial image (bottom row for this column)\n",
    "        ax_adv = axes[row * 2 + 1, col]\n",
    "        adv_img = mnist_denormalize(results[idx]['perturbed_image'].squeeze()).numpy()\n",
    "        ax_adv.imshow(adv_img, cmap='gray', vmin=0, vmax=1)\n",
    "\n",
    "        title_color = MALWARE_RED if results[idx]['success'] else HACKER_GREY\n",
    "        ax_adv.set_title(f\"Adversarial: {results[idx]['adversarial_label']}\",\n",
    "                        color=title_color, fontsize=10)\n",
    "        ax_adv.axis('off')\n",
    "\n",
    "    plt.suptitle('DeepFool Attack: Original vs Adversarial Examples',\n",
    "                color=HTB_GREEN, fontsize=16, y=0.98)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(os.path.join(save_dir, 'deepfool_examples.png'),\n",
    "                facecolor=NODE_BLACK, dpi=150, bbox_inches='tight')\n",
    "    plt.close()\n",
    "\n",
    "    print(f\"Grid visualization saved to {save_dir}/deepfool_examples.png\")\n",
    "\n",
    "# Generate the grid visualization\n",
    "visualize_attack_grid(results, save_dir='output')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb22c6c-84d5-4204-a21a-ab95464e67a1",
   "metadata": {},
   "source": [
    "# Perturbation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3a9c8b1-3822-4477-901c-e1e30f84e650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generating perturbation analysis...\n",
      "Perturbation analysis saved to output/deepfool_perturbations.png\n"
     ]
    }
   ],
   "source": [
    "def visualize_perturbation_analysis(results, save_dir='output'):\n",
    "    \"\"\"\n",
    "    Analyze and visualize perturbation characteristics across samples.\n",
    "\n",
    "    Creates two-row visualization: top shows raw perturbation heatmaps,\n",
    "    bottom shows amplified differences overlaid on originals.\n",
    "\n",
    "    Args:\n",
    "        results (list): Attack results\n",
    "        save_dir (str): Output directory\n",
    "    \"\"\"\n",
    "    print(\"\\nGenerating perturbation analysis...\")\n",
    "\n",
    "    fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
    "    fig.patch.set_facecolor(NODE_BLACK)\n",
    "\n",
    "    for ax in axes.flatten():\n",
    "        ax.set_facecolor(NODE_BLACK)\n",
    "        for spine in ax.spines.values():\n",
    "            spine.set_edgecolor(HACKER_GREY)\n",
    "\n",
    "    # Select first 3 successful attacks\n",
    "    successful_attacks = [r for r in results if r['success']][:3]\n",
    "\n",
    "    for idx, result in enumerate(successful_attacks):\n",
    "        # Top row: Raw perturbation heatmap\n",
    "        ax_top = axes[0, idx]\n",
    "        pert = result['perturbation'].squeeze().numpy()\n",
    "        vmax = np.abs(pert).max() or 1e-6\n",
    "        im_top = ax_top.imshow(pert, cmap='RdBu_r', vmin=-vmax, vmax=vmax)\n",
    "        ax_top.set_title(f'Perturbation (L2={result[\"l2_norm\"]:.3f})',\n",
    "                        color=HTB_GREEN, fontsize=10)\n",
    "        ax_top.axis('off')\n",
    "\n",
    "        cbar_top = plt.colorbar(im_top, ax=ax_top, fraction=0.046, pad=0.04)\n",
    "        cbar_top.outline.set_edgecolor(HACKER_GREY)\n",
    "        cbar_top.ax.tick_params(colors=WHITE)\n",
    "\n",
    "        # Bottom row: Amplified difference visualization\n",
    "        ax_bottom = axes[1, idx]\n",
    "        orig_img = result['original_image'].squeeze().numpy()\n",
    "        adv_img = result['perturbed_image'].squeeze().detach().numpy()\n",
    "        diff_amplified = (adv_img - orig_img) * 10  # 10x amplification for visibility\n",
    "\n",
    "        im_bottom = ax_bottom.imshow(diff_amplified, cmap='RdBu_r', vmin=-0.5, vmax=0.5)\n",
    "        ax_bottom.set_title(f\"{result['original_label']} → {result['adversarial_label']} \"\n",
    "                           f\"({result['iterations']} iters)\",\n",
    "                           color=NUGGET_YELLOW, fontsize=10)\n",
    "        ax_bottom.axis('off')\n",
    "\n",
    "        cbar_bottom = plt.colorbar(im_bottom, ax=ax_bottom, fraction=0.046, pad=0.04)\n",
    "        cbar_bottom.outline.set_edgecolor(HACKER_GREY)\n",
    "        cbar_bottom.ax.tick_params(colors=WHITE)\n",
    "\n",
    "    plt.suptitle('DeepFool Perturbation Analysis', color=HTB_GREEN, fontsize=16, y=0.98)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(os.path.join(save_dir, 'deepfool_perturbations.png'),\n",
    "                facecolor=NODE_BLACK, dpi=150, bbox_inches='tight')\n",
    "    plt.close()\n",
    "\n",
    "    print(f\"Perturbation analysis saved to {save_dir}/deepfool_perturbations.png\")\n",
    "\n",
    "# Generate the perturbation analysis visualization\n",
    "visualize_perturbation_analysis(results, save_dir='output')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "36d1ccf4-f279-4e98-9339-94343318808e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generating attack metrics visualization...\n",
      "L2 norm range: [0.5942, 8.1619]\n",
      "Iteration range: [1, 4]\n",
      "Metrics visualization saved to output/deepfool_metrics.png\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nGenerating attack metrics visualization...\")\n",
    "\n",
    "# Setup three-panel figure\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "fig.patch.set_facecolor(NODE_BLACK)\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_facecolor(NODE_BLACK)\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor(HACKER_GREY)\n",
    "    ax.tick_params(colors=WHITE)\n",
    "    ax.grid(True, alpha=0.3, color=HACKER_GREY, linestyle='--')\n",
    "\n",
    "# Panel 1: L2 Norm Distribution\n",
    "l2_norms = [r['l2_norm'] for r in results]\n",
    "axes[0].hist(l2_norms, bins=15, color=HTB_GREEN, alpha=0.7, edgecolor=HACKER_GREY)\n",
    "axes[0].set_xlabel('L2 Norm', color=WHITE)\n",
    "axes[0].set_ylabel('Frequency', color=WHITE)\n",
    "axes[0].set_title('Perturbation Magnitude Distribution', color=HTB_GREEN)\n",
    "print(f\"L2 norm range: [{min(l2_norms):.4f}, {max(l2_norms):.4f}]\")\n",
    "\n",
    "# Panel 2: Iteration Count Distribution\n",
    "iterations = [r['iterations'] for r in results]\n",
    "axes[1].hist(iterations, bins=range(1, max(iterations)+2),\n",
    "            color=AZURE, alpha=0.7, edgecolor=HACKER_GREY)\n",
    "axes[1].set_xlabel('Iterations', color=WHITE)\n",
    "axes[1].set_ylabel('Frequency', color=WHITE)\n",
    "axes[1].set_title('Iterations Required', color=HTB_GREEN)\n",
    "print(f\"Iteration range: [{min(iterations)}, {max(iterations)}]\")\n",
    "\n",
    "# Panel 3: Per-Class Success Rates\n",
    "class_success = {}\n",
    "for r in results:\n",
    "    orig = r['original_label']\n",
    "    if orig not in class_success:\n",
    "        class_success[orig] = {'total': 0, 'success': 0}\n",
    "    class_success[orig]['total'] += 1\n",
    "    if r['success']:\n",
    "        class_success[orig]['success'] += 1\n",
    "\n",
    "classes = sorted(class_success.keys())\n",
    "success_rates = [\n",
    "    class_success[c]['success'] / class_success[c]['total'] * 100\n",
    "    if class_success[c]['total'] > 0 else 0\n",
    "    for c in classes\n",
    "]\n",
    "\n",
    "bars = axes[2].bar(classes, success_rates, color=NUGGET_YELLOW,\n",
    "                   alpha=0.7, edgecolor=HACKER_GREY)\n",
    "axes[2].set_xlabel('Original Class', color=WHITE)\n",
    "axes[2].set_ylabel('Success Rate (%)', color=WHITE)\n",
    "axes[2].set_title('Attack Success by Class', color=HTB_GREEN)\n",
    "axes[2].set_ylim(0, 105)\n",
    "\n",
    "# Add percentage labels on bars\n",
    "for bar, rate in zip(bars, success_rates):\n",
    "    height = bar.get_height()\n",
    "    ax_x = bar.get_x() + bar.get_width() / 2.0\n",
    "    axes[2].text(ax_x, height + 1, f'{rate:.0f}%',\n",
    "                 ha='center', va='bottom', color=WHITE, fontsize=8)\n",
    "\n",
    "# Save visualization\n",
    "plt.suptitle('DeepFool Attack Metrics', color=HTB_GREEN, fontsize=16, y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.savefig('output/deepfool_metrics.png',\n",
    "            facecolor=NODE_BLACK, dpi=150, bbox_inches='tight')\n",
    "plt.close()\n",
    "\n",
    "print(\"Metrics visualization saved to output/deepfool_metrics.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ff85990-5ca2-441a-b7c1-429291de15bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Attack Summary Statistics\n",
      "============================================================\n",
      "Success Rate: 20/20 (100.0%)\n",
      "Average L2 Norm: 4.7254\n",
      "L2 Range: [0.5942, 8.1619]\n",
      "Average Iterations: 2.7\n",
      "\n",
      "Most Common Misclassifications:\n",
      "  9→4: 4 times\n",
      "  7→3: 2 times\n",
      "  0→6: 2 times\n",
      "  4→9: 2 times\n",
      "  2→6: 1 times\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "def print_summary_statistics(results):\n",
    "    \"\"\"\n",
    "    Print summary statistics for attack results.\n",
    "\n",
    "    Computes and displays success rate, perturbation statistics, iteration\n",
    "    statistics, and common class transitions.\n",
    "\n",
    "    Args:\n",
    "        results (list): Attack results\n",
    "    \"\"\"\n",
    "    print(\"\\n\" + \"=\"*60)\n",
    "    print(\"Attack Summary Statistics\")\n",
    "    print(\"=\"*60)\n",
    "\n",
    "    successful_attacks = [r for r in results if r['success']]\n",
    "\n",
    "    if successful_attacks:\n",
    "        avg_l2 = np.mean([r['l2_norm'] for r in successful_attacks])\n",
    "        avg_iterations = np.mean([r['iterations'] for r in successful_attacks])\n",
    "        min_l2 = min([r['l2_norm'] for r in successful_attacks])\n",
    "        max_l2 = max([r['l2_norm'] for r in successful_attacks])\n",
    "\n",
    "        print(f\"Success Rate: {len(successful_attacks)}/{len(results)} \"\n",
    "              f\"({100*len(successful_attacks)/len(results):.1f}%)\")\n",
    "        print(f\"Average L2 Norm: {avg_l2:.4f}\")\n",
    "        print(f\"L2 Range: [{min_l2:.4f}, {max_l2:.4f}]\")\n",
    "        print(f\"Average Iterations: {avg_iterations:.1f}\")\n",
    "\n",
    "        # Class transition analysis\n",
    "        transitions = {}\n",
    "        for r in successful_attacks:\n",
    "            key = f\"{r['original_label']}→{r['adversarial_label']}\"\n",
    "            transitions[key] = transitions.get(key, 0) + 1\n",
    "\n",
    "        print(f\"\\nMost Common Misclassifications:\")\n",
    "        for trans, count in sorted(transitions.items(), key=lambda x: x[1], reverse=True)[:5]:\n",
    "            print(f\"  {trans}: {count} times\")\n",
    "    else:\n",
    "        print(\"No successful attacks generated\")\n",
    "\n",
    "    print(\"=\"*60)\n",
    "\n",
    "# Generate summary\n",
    "print_summary_statistics(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22bb380b-3504-4c01-8496-1a10a603903e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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