Agent Conversation: google/gemini-3.1-pro-preview

Model: google/gemini-3.1-pro-preview
Total steps: 7
Messages: 27
UserStep 0

Task: security-adversarial-attack-sparse-l0

Sparse Adversarial Attack (L0 Constraint)

Objective

Implement a stronger sparse attack in bench/custom_attack.py. Your method should maximize attack success rate (ASR) under a strict L0 perturbation budget:

  • Threat model: full model access for custom attack implementation.
  • Norm constraint: number of modified spatial pixels is bounded.
  • Budget: L0(x_adv, x) <= pixels, where pixels = 10.

Editable Interface

You must implement:

run_attack(model, images, labels, pixels, device, n_classes) -> adv_images

Inputs:

  • images: tensor of shape (N, C, H, W), values in [0, 1].
  • labels: tensor of shape (N,).
  • pixels: maximum number of modified spatial pixels per sample.
  • n_classes: 10 for CIFAR-10, 100 for CIFAR-100.

Output:

  • adv_images: same shape as images, also in [0, 1].

Evaluation Protocol

Each evaluation script:

  1. Loads one pretrained model.
  2. Collects up to 1000 samples that are initially classified correctly.
  3. Runs your run_attack.
  4. Checks L0 validity (<= pixels modified spatial pixels).
  5. Reports:
    • clean_acc
    • robust_acc
    • asr = 1 - robust_acc

Important:

  • ASR denominator is the number of initially correct samples.
  • Invalid adversarial outputs (shape mismatch or violated budget) are treated as failure.

Scenarios

Six scenarios are evaluated in parallel:

  • ResNet20 on CIFAR-10
  • VGG11-BN on CIFAR-10
  • MobileNetV2 on CIFAR-10
  • ResNet20 on CIFAR-100
  • VGG11-BN on CIFAR-100
  • MobileNetV2 on CIFAR-100

Baselines

  • onepixel: one-pixel differential evolution based sparse baseline.
  • sparsefool: gradient-based sparse perturbation baseline.
  • jsma: Jacobian saliency map based targeted sparse baseline.
  • pixle: pixel rearrangement based sparse baseline.

Your goal is to improve ASR while respecting the L0 budget.

torchattacks/bench/custom_attack.py [EDITABLE — lines 3–26 only]

     1: import torch
     2: import torch.nn as nn
     3: 
     4: # =====================================================================
     5: # EDITABLE: implement run_attack below
     6: # =====================================================================
     7: def run_attack(
     8:     model: nn.Module,
     9:     images: torch.Tensor,
    10:     labels: torch.Tensor,
    11:     pixels: int,
    12:     device: torch.device,
    13:     n_classes: int,
    14: ) -> torch.Tensor:
    15:     """
    16:     Sparse L0 adversarial attack.
    17:     images: (N, C, H, W) in [0, 1] on device. labels: (N,) on device.
    18:     pixels: max number of modified spatial pixels (H, W) per sample.
    19:     n_classes: 10 for CIFAR-10, 100 for CIFAR-100.
    20:     Returns adv_images satisfying an L0 pixel budget validated by evaluator.
    21:     """
    22:     _ = (model, labels, pixels, device, n_classes)
    23:     return images.clone()
    24: 
    25: # =======================================================
AssistantStep 1edit
Tool Input
1{
2  "filename": "torchattacks/bench/custom_attack.py",
3  "start_line": 7,
4  "content": "def run_attack(\n    model: nn.Module,\n    images: torch.Tensor,\n    labels: torch.Tensor,\n    pixels: int,\n    device: torch.device,\n    n_classes: int,\n) -> torch.Tensor:\n    \"\"\"\n    Sparse L0 adversarial attack.\n    images: (N, C, H, W) in [0, 1] on device. labels: (N,) on device.\n    pixels: max number of modified spatial pixels (H, W) per sample.\n    n_classes: 10 for CIFAR-10, 100 for CIFAR-100.\n    \"\"\"\n    import torch.nn.functional as F\n\n    model.eval()\n    batch_size, C, H, W_dim = images.shape\n    \n    W = torch.empty_like(images).uniform_(-0.05, 0.05)\n    W.requires_grad_()\n    \n    steps = 150\n    lr = 0.1\n    kappa = 0.1\n    \n    optimizer = torch.optim.Adam([W], lr=lr)\n    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=steps)\n    \n    best_adv = images.clone().detach()\n    best_margin = torch.full((batch_size,), 1e9, device=device)\n    success = torch.zeros(batch_size, dtype=torch.bool, device=device)\n    \n    for step in range(steps):\n        with torch.no_grad():\n            W.clamp_(-2.0, 2.0)\n            \n        score = W.abs().sum(dim=1)\n        score_flat = score.view(batch_size, -1)\n        \n        topk_idx = torch.topk(score_flat, pixels, dim=1).indices\n        mask_flat = torch.zeros_like(score_flat).scatter_(1, topk_idx, 1.0)\n        mask = mask_flat.view(batch_size, 1, H, W_dim)\n        \n        delta_unmasked = torch.clamp(images + W, 0.0, 1.0) - images\n        delta = delta_unmasked * mask\n        \n        images_adv = images + W + (delta - W).detach()\n        images_adv = torch.clamp(images_adv, 0.0, 1.0)\n        \n        logits = model(images_adv)\n        \n        with torch.no_grad():\n            labels_onehot = F.one_hot(labels, num_classes=n_classes)\n            real_logits = (logits * labels_onehot).sum(1)\n            other_logits = (logits - labels_onehot * 10000.0).max(1)[0]\n            \n            margin = real_logits - other_logits\n            is_adv = margin < 0\n            \n            improved = margin < best_margin\n            best_adv[improved] = images_adv[improved].clone().detach()\n            best_margin[improved] = margin[improved]\n            \n            success = success | is_adv\n            if success.all():\n                break\n                \n        loss_val = torch.clamp(margin + kappa, min=0.0)\n        loss = loss_val.mean()\n        \n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        scheduler.step()\n\n    return best_adv",
5  "end_line": 24,
6  "op": "replace"
7}
Tool ResultStep 1
OK: Replaced lines 7..24 with 77 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–85.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–85 | total: 86 lines]
     1| import torch
     2| import torch.nn as nn
     3  
     4  # =====================================================================
     5  # EDITABLE: implement run_attack below
     6  # =====================================================================
     7  def run_attack(
     8      model: nn.Module,
     9      images: torch.Tensor,
    10      labels: torch.Tensor,
    11      pixels: int,
    12      device: torch.device,
    13      n_classes: int,
    14  ) -> torch.Tensor:
    15      """
    16      Sparse L0 adversarial attack.
    17      images: (N, C, H, W) in [0, 1] on device. labels: (N,) on device.
    18      pixels: max number of modified spatial pixels (H, W) per sample.
    19      n_classes: 10 for CIFAR-10, 100 for CIFAR-100.
    20      """
    21      import
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### ResNet20-C10 (scripts/eval_resnet20_cifar10.sh)
Using cache found in /srv/home/bohanlyu/MLS-Bench/vendor/data/torch_cache/hub/chenyaofo_pytorch-cifar-models_master
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/run_eval.py", line 177, in <module>
    main()
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/run_eval.py", line 123, in main
    adv_images = run_attack(attack_model, images, labels, args.pixels, device, n_classes)
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/custom_attack.py", line 79, in run_attack
    loss.backward()
  File "/home/bohanlyu/m
AssistantStep 3edit
Tool Input
1{
2  "start_line": 7,
3  "content": "def run_attack(\n    model: nn.Module,\n    images: torch.Tensor,\n    labels: torch.Tensor,\n    pixels: int,\n    device: torch.device,\n    n_classes: int,\n) -> torch.Tensor:\n    import torch.nn.functional as F\n\n    model.eval()\n    batch_size, C, H, W_dim = images.shape\n    \n    W = torch.empty_like(images).uniform_(-0.05, 0.05)\n    W.requires_grad_()\n    \n    steps = 150\n    lr = 0.1\n    kappa = 0.1\n    \n    optimizer = torch.optim.Adam([W], lr=lr)\n    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=steps)\n    \n    best_adv = images.clone().detach()\n    best_margin = torch.full((batch_size,), 1e9, device=device)\n    success = torch.zeros(batch_size, dtype=torch.bool, device=device)\n    \n    for step in range(steps):\n        with torch.no_grad():\n            W.clamp_(-2.0, 2.0)\n            \n        score = W.abs().sum(dim=1)\n        score_flat = score.view(batch_size, -1)\n        \n        topk_idx = torch.topk(score_flat, pixels, dim=1).indices\n        mask_flat = torch.zeros_like(score_flat).scatter_(1, topk_idx, 1.0)\n        mask = mask_flat.view(batch_size, 1, H, W_dim)\n        \n        delta_unmasked = torch.clamp(images + W, 0.0, 1.0) - images\n        delta = delta_unmasked * mask\n        \n        images_adv = images + W + (delta - W).detach()\n        images_adv = torch.clamp(images_adv, 0.0, 1.0)\n        \n        logits = model(images_adv)\n        \n        with torch.no_grad():\n            labels_onehot = F.one_hot(labels, num_classes=n_classes).to(logits.dtype)\n            real_logits = (logits * labels_onehot).sum(1)\n            other_logits = (logits - labels_onehot * 10000.0).max(1)[0]\n            \n            margin = real_logits - other_logits\n            is_adv = margin < 0\n            \n            improved = margin < best_margin\n            best_adv[improved] = images_adv[improved].clone().detach()\n            best_margin[improved] = margin[improved]\n            \n            success = success | is_adv\n            if success.all():\n                break\n                \n        loss_val = torch.clamp(margin + kappa, min=0.0)\n        loss = loss_val.mean()\n        \n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        scheduler.step()\n\n    return best_adv",
4  "filename": "torchattacks/bench/custom_attack.py",
5  "end_line": 83,
6  "op": "replace"
7}
Tool ResultStep 3
OK: Replaced lines 7..83 with 71 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–79.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–79 | total: 80 lines]
     1| import torch
     2| import torch.nn as nn
     3  
     4  # =====================================================================
     5  # EDITABLE: implement run_attack below
     6  # =====================================================================
     7  def run_attack(
     8      model: nn.Module,
     9      images: torch.Tensor,
    10      labels: torch.Tensor,
    11      pixels: int,
    12      device: torch.device,
    13      n_classes: int,
    14  ) -> torch.Tensor:
    15      import torch.nn.functional as F
    16  
    17      model.eval()
    18      batch_size, C, H, W_dim = images.shape
    19      
    20      W = torch.empty_like(images).uniform_(-0.05, 0.05)
    21      W.requires_grad_()
    22      
    23      steps = 150
    24      lr = 0.1
    25
AssistantStep 4test
Tool ResultStep 4
[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### ResNet20-C10 (scripts/eval_resnet20_cifar10.sh)
Using cache found in /srv/home/bohanlyu/MLS-Bench/vendor/data/torch_cache/hub/chenyaofo_pytorch-cifar-models_master
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/run_eval.py", line 177, in <module>
    main()
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/run_eval.py", line 123, in main
    adv_images = run_attack(attack_model, images, labels, args.pixels, device, n_classes)
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/custom_attack.py", line 73, in run_attack
    loss.backward()
  File "/home/bohanlyu/mi
AssistantStep 5edit
Tool Input
1{
2  "filename": "torchattacks/bench/custom_attack.py",
3  "start_line": 7,
4  "end_line": 77,
5  "op": "replace",
6  "content": "def run_attack(\n    model: nn.Module,\n    images: torch.Tensor,\n    labels: torch.Tensor,\n    pixels: int,\n    device: torch.device,\n    n_classes: int,\n) -> torch.Tensor:\n    import torch.nn.functional as F\n\n    model.eval()\n    batch_size, C, H, W_dim = images.shape\n    \n    W = torch.empty_like(images).uniform_(-0.05, 0.05)\n    W.requires_grad_()\n    \n    steps = 150\n    lr = 0.1\n    kappa = 0.1\n    \n    optimizer = torch.optim.Adam([W], lr=lr)\n    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=steps)\n    \n    best_adv = images.clone().detach()\n    best_margin = torch.full((batch_size,), 1e9, device=device)\n    success = torch.zeros(batch_size, dtype=torch.bool, device=device)\n    \n    for step in range(steps):\n        with torch.no_grad():\n            W.clamp_(-2.0, 2.0)\n            \n            score = W.abs().sum(dim=1)\n            score_flat = score.view(batch_size, -1)\n            \n            topk_idx = torch.topk(score_flat, pixels, dim=1).indices\n            mask_flat = torch.zeros(batch_size, score_flat.shape[1], device=device).scatter_(1, topk_idx, 1.0)\n            mask = mask_flat.view(batch_size, 1, H, W_dim)\n            \n            delta_unmasked = torch.clamp(images + W, 0.0, 1.0) - images\n            delta = delta_unmasked * mask\n        \n        images_adv = images + W + (delta - W).detach()\n        images_adv = torch.clamp(images_adv, 0.0, 1.0)\n        \n        logits = model(images_adv)\n        \n        with torch.no_grad():\n            labels_onehot = F.one_hot(labels, num_classes=n_classes).to(logits.dtype)\n            real_logits = (logits * labels_onehot).sum(1)\n            other_logits = (logits - labels_onehot * 10000.0).max(1)[0]\n            \n            margin = real_logits - other_logits\n            is_adv = margin < 0\n            \n            improved = margin < best_margin\n            best_adv[improved] = images_adv[improved].clone().detach()\n            best_margin[improved] = margin[improved]\n            \n            success = success | is_adv\n            if success.all():\n                break\n                \n        loss_val = torch.clamp(margin + kappa, min=0.0)\n        loss = loss_val.mean()\n        \n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        scheduler.step()\n\n    return best_adv"
7}
Tool ResultStep 5
OK: Replaced lines 7..77 with 71 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–79.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–79 | total: 80 lines]
     1| import torch
     2| import torch.nn as nn
     3  
     4  # =====================================================================
     5  # EDITABLE: implement run_attack below
     6  # =====================================================================
     7  def run_attack(
     8      model: nn.Module,
     9      images: torch.Tensor,
    10      labels: torch.Tensor,
    11      pixels: int,
    12      device: torch.device,
    13      n_classes: int,
    14  ) -> torch.Tensor:
    15      import torch.nn.functional as F
    16  
    17      model.eval()
    18      batch_size, C, H, W_dim = images.shape
    19      
    20      W = torch.empty_like(images).uniform_(-0.05, 0.05)
    21      W.requires_grad_()
    22      
    23      steps = 150
    24      lr = 0.1
    25
AssistantStep 6test
Tool ResultStep 6
[Test #3] (0 tests remaining)

### ResNet20-C10 (scripts/eval_resnet20_cifar10.sh)
Using cache found in /srv/home/bohanlyu/MLS-Bench/vendor/data/torch_cache/hub/chenyaofo_pytorch-cifar-models_master
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/run_eval.py", line 177, in <module>
    main()
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/run_eval.py", line 123, in main
    adv_images = run_attack(attack_model, images, labels, args.pixels, device, n_classes)
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/security-adversarial-attack-sparse-l0/google_gemini-3.1-pro-preview_20260402_182721/torchattacks/bench/custom_attack.py", line 73, in run_attack
    loss.backward()
  File "/home/bohanlyu/miniconda3/envs/mlsbench-torchattacks/lib
AssistantStep 1edit
Tool Input
1{
2  "end_line": 24,
3  "filename": "torchattacks/bench/custom_attack.py",
4  "op": "replace",
5  "content": "def run_attack(\n    model: nn.Module,\n    images: torch.Tensor,\n    labels: torch.Tensor,\n    pixels: int,\n    device: torch.device,\n    n_classes: int,\n) -> torch.Tensor:\n    N, C, H, W = images.shape\n    best_adv = images.clone()\n    \n    model.eval()\n    idx = torch.arange(N, device=device)\n    \n    with torch.no_grad():\n        initial_logits = model(images)\n        pred = initial_logits.argmax(dim=1)\n        \n        logits_copy = initial_logits.clone()\n        logits_copy[idx, labels] = -1e9\n        \n    restarts = 5\n    restarts = min(restarts, n_classes - 1)\n    _, top_targets = torch.topk(logits_copy, restarts, dim=1)\n    \n    misclassified = pred != labels\n    \n    for r in range(restarts + 1):\n        if misclassified.all():\n            break\n            \n        is_untargeted = (r == restarts)\n        if not is_untargeted:\n            targets = top_targets[:, r]\n            \n        mask = torch.zeros(N, 1, H, W, device=device)\n        delta = torch.zeros_like(images, device=device)\n        \n        for p in range(pixels):\n            adv = (images + delta).detach()\n            adv.requires_grad = True\n            logits = model(adv)\n            \n            with torch.no_grad():\n                pred_curr = logits.argmax(dim=1)\n                success = pred_curr != labels\n                new_success = success & (~misclassified)\n                if new_success.any():\n                    best_adv[new_success] = adv[new_success].clone().detach()\n                    misclassified = misclassified | success\n            \n            if is_untargeted:\n                Z_y = logits[idx, labels]\n                logits_other = logits.clone()\n                logits_other[idx, labels] = -1e9\n                Z_other = logits_other.max(dim=1)[0]\n                loss = (Z_other - Z_y).sum()\n            else:\n                Z_y = logits[idx, labels]\n                Z_t = logits[idx, targets]\n                loss = (Z_t - Z_y).sum()\n                \n            grad = torch.autograd.grad(loss, adv)[0]\n            \n            potential = torch.where(grad > 0, grad * (1.0 - adv), grad * (0.0 - adv))\n            score = potential.sum(dim=1, keepdim=True)\n            score = score * (1 - mask)\n            \n            score_flat = score.view(N, -1)\n            _, max_idx = torch.max(score_flat, dim=1)\n            \n            mask_flat = mask.view(N, -1)\n            mask_flat.scatter_(1, max_idx.unsqueeze(1), 1.0)\n            mask = mask_flat.view(N, 1, H, W)\n            \n            for _ in range(5):\n                adv = (images + delta).detach()\n                adv.requires_grad = True\n                logits = model(adv)\n                \n                with torch.no_grad():\n                    pred_inner = logits.argmax(dim=1)\n                    success_inner = pred_inner != labels\n                    new_success = success_inner & (~misclassified)\n                    if new_success.any():\n                        best_adv[new_success] = adv[new_success].clone().detach()\n                        misclassified = misclassified | success_inner\n                \n                if is_untargeted:\n                    Z_y = logits[idx, labels]\n                    logits_other = logits.clone()\n                    logits_other[idx, labels] = -1e9\n                    Z_other = logits_other.max(dim=1)[0]\n                    loss = (Z_other - Z_y).sum()\n                else:\n                    Z_y = logits[idx, labels]\n                    Z_t = logits[idx, targets]\n                    loss = (Z_t - Z_y).sum()\n                \n                grad = torch.autograd.grad(loss, adv)[0]\n                \n                delta = delta + 0.2 * grad.sign() * mask\n                delta = torch.clamp(images + delta, 0.0, 1.0) - images\n                delta = delta * mask\n        \n        alpha_final = 0.1\n        for _ in range(20):\n            adv = (images + delta).detach()\n            adv.requires_grad = True\n            logits = model(adv)\n            \n            with torch.no_grad():\n                pred_final = logits.argmax(dim=1)\n                success_final = pred_final != labels\n                new_success = success_final & (~misclassified)\n                if new_success.any():\n                    best_adv[new_success] = adv[new_success].clone().detach()\n                    misclassified = misclassified | success_final\n            \n            Z_y = logits[idx, labels]\n            logits_other = logits.clone()\n            logits_other[idx, labels] = -1e9\n            Z_other = logits_other.max(dim=1)[0]\n            loss = (Z_other - Z_y).sum()\n            \n            grad = torch.autograd.grad(loss, adv)[0]\n            \n            delta = delta + alpha_final * grad.sign() * mask\n            delta = torch.clamp(images + delta, 0.0, 1.0) - images\n            delta = delta * mask\n            \n            alpha_final *= 0.9\n\n    return best_adv",
6  "start_line": 7,
7  "after_line": 6
8}
Tool ResultStep 1
OK: Replaced lines 7..24 with 134 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–142.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–142 | total: 143 lines]
     1| import torch
     2| import torch.nn as nn
     3  
     4  # =====================================================================
     5  # EDITABLE: implement run_attack below
     6  # =====================================================================
     7  def run_attack(
     8      model: nn.Module,
     9      images: torch.Tensor,
    10      labels: torch.Tensor,
    11      pixels: int,
    12      device: torch.device,
    13      n_classes: int,
    14  ) -> torch.Tensor:
    15      N, C, H, W = images.shape
    16      best_adv = images.clone()
    17      
    18      model.eval()
    19      idx = torch.arange(N, device=device)
    20      
    21      with torch.no_grad():
    22          initial_logits = model(images)
    23          pred = initial_logits.argmax(
AssistantStep 2test
Tool ResultStep 2
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### ResNet20-C10 (scripts/eval_resnet20_cifar10.sh)
Attack results (ResNet20-C10): ASR=0.9900, clean_acc=1.0000, robust_acc=0.0100

### VGG11BN-C10 (scripts/eval_vgg11bn_cifar10.sh)
Attack results (VGG11BN-C10): ASR=0.9800, clean_acc=1.0000, robust_acc=0.0200

### MobileNetV2-C10 (scripts/eval_mobilenetv2_cifar10.sh)
Attack results (MobileNetV2-C10): ASR=0.9800, clean_acc=1.0000, robust_acc=0.0200

### ResNet20-C100 (scripts/eval_resnet20_cifar100.sh)
Attack results (ResNet20-C100): ASR=0.9500, clean_acc=1.0000, robust_acc=0.0500

### VGG11BN-C100 (scripts/eval_vgg11bn_cifar100.sh)
Attack results (VGG11BN-C100): ASR=0.9300, clean_acc=1.0000, robust_acc=0.0700

[Leaderboard] Results saved: {'asr_ResNet20_C10': 0.99, 'elapsed_ResNet20-C10': 18, 'asr_VGG11BN_C10': 0.98, 'elapsed_VGG11BN-C10': 18, 'asr_MobileNetV2_C10': 0.98, 'elapsed_MobileNetV2-C10': 31, 'asr_ResNet20_C100': 0.95, 'elapsed_ResNet20-C100': 31, 'asr_VGG
AssistantStep 3edit
Tool Input
1{
2  "content": "        alpha_final = 0.1\n        for _ in range(40):\n            adv = (images + delta).detach()\n            adv.requires_grad = True\n            logits = model(adv)\n            \n            with torch.no_grad():\n                pred_final = logits.argmax(dim=1)\n                success_final = pred_final != labels\n                new_success = success_final & (~misclassified)\n                if new_success.any():\n                    best_adv[new_success] = adv[new_success].clone().detach()\n                    misclassified = misclassified | success_final\n            \n            Z_y = logits[idx, labels]\n            logits_other = logits.clone()\n            logits_other[idx, labels] = -1e9\n            Z_other = logits_other.max(dim=1)[0]\n            loss = (Z_other - Z_y).sum()\n            \n            grad = torch.autograd.grad(loss, adv)[0]\n            \n            delta = delta + alpha_final * grad.sign() * mask\n            delta = torch.clamp(images + delta, 0.0, 1.0) - images\n            delta = delta * mask\n            \n            alpha_final *= 0.95",
3  "after_line": 1,
4  "end_line": 138,
5  "filename": "torchattacks/bench/custom_attack.py",
6  "start_line": 112,
7  "op": "replace"
8}
Tool ResultStep 3
OK: Replaced lines 112..138 with 27 line(s) in torchattacks/bench/custom_attack.py. Editable range: 3–142.

[Current file: torchattacks/bench/custom_attack.py | editable: 3–142 | total: 143 lines]
     1| import torch
     2| import torch.nn as nn
     3  
     4  # =====================================================================
     5  # EDITABLE: implement run_attack below
     6  # =====================================================================
     7  def run_attack(
     8      model: nn.Module,
     9      images: torch.Tensor,
    10      labels: torch.Tensor,
    11      pixels: int,
    12      device: torch.device,
    13      n_classes: int,
    14  ) -> torch.Tensor:
    15      N, C, H, W = images.shape
    16      best_adv = images.clone()
    17      
    18      model.eval()
    19      idx = torch.arange(N, device=device)
    20      
    21      with torch.no_grad():
    22          initial_logits = model(images)
    23          pred = initial_logits.argma
AssistantStep 4edit
Tool Input
1{
2  "op": "replace",
3  "start_line": 28,
4  "filename": "torchattacks/bench/custom_attack.py",
5  "content": "    restarts = 9\n    restarts = min(restarts, n_classes - 1)",
6  "end_line": 29,
7  "after_line": 27
8}