"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: ship (8)\n",
"Image #2: automobile (1)\n",
"Image #3: frog (6)\n",
"Image #4: ship (8)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: ship (8)\n",
"Image #2: automobile (1)\n",
"Image #3: frog (6)\n",
"Image #4: ship (8)\n",
"[Validation] Loss: 0.0031 Accuracy: 95.2800% Time elapsed: 7.0879s (total 10000 images)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c_EVasbm7Pao"
},
"source": [
"#### Prepare Adversarial Attack Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8Rwgci4t7OIz",
"outputId": "5788a277-4f41-4f78-98b4-5e28577aa43b"
},
"source": [
"!pip install foolbox\n",
"!pip install advertorch"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: foolbox in /usr/local/lib/python3.7/dist-packages (3.3.1)\n",
"Requirement already satisfied: eagerpy==0.29.0 in /usr/local/lib/python3.7/dist-packages (from foolbox) (0.29.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.1 in /usr/local/lib/python3.7/dist-packages (from foolbox) (3.7.4.3)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from foolbox) (1.4.1)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from foolbox) (1.19.5)\n",
"Requirement already satisfied: requests>=2.24.0 in /usr/local/lib/python3.7/dist-packages (from foolbox) (2.25.1)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from foolbox) (57.0.0)\n",
"Requirement already satisfied: GitPython>=3.0.7 in /usr/local/lib/python3.7/dist-packages (from foolbox) (3.1.17)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (2020.12.5)\n",
"Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (3.0.4)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (1.24.3)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->foolbox) (2.10)\n",
"Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.7/dist-packages (from GitPython>=3.0.7->foolbox) (4.0.7)\n",
"Requirement already satisfied: smmap<5,>=3.0.1 in /usr/local/lib/python3.7/dist-packages (from gitdb<5,>=4.0.1->GitPython>=3.0.7->foolbox) (4.0.0)\n",
"Requirement already satisfied: advertorch in /usr/local/lib/python3.7/dist-packages (0.2.3)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "a-bT17uz7QPU"
},
"source": [
"def get_distance(a, b):\n",
" l0 = torch.norm((a - b).view(a.shape[0], -1), p=0, dim=1)\n",
" l2 = torch.norm((a - b).view(a.shape[0], -1), p=2, dim=1)\n",
" mse = (a - b).view(a.shape[0], -1).pow(2).mean(1)\n",
" linf = torch.norm((a - b).view(a.shape[0], -1), p=float('inf'), dim=1)\n",
" return l0, l2, mse, linf"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "yNO0lKnzeU2y"
},
"source": [
"#### HopSkipJumpAttack\n",
"\n",
"* Foolbox documentation: https://foolbox.readthedocs.io/en/stable/\n",
"* HopSkipJumpAttack implementation: [HopSkipJumpAttack](https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/hop_skip_jump.py)\n",
"* init_attack: Attack to use to find starting points.\n",
" * The targeted attack does not use this parameter.\n",
" * The untargeted attack can use a random uniform noise.\n",
"* steps: Number of optimization steps.\n",
"* initial_gradient_eval_steps: Initial number of evaluations for gradient estimation.\n",
"* max_gradient_eval_steps: Maximum number of evaluations for gradient estimation.\n",
"* stepsize_search: \"geometric_progression\" or \"grid_search\".\n",
"* gamma: It's for the binary search threshold.\n",
"* constraint: Norm to minimize, either \"l2\" or \"linf\".\n",
"\n",
"\n",
"def __init__(\n",
" self,\n",
" init_attack: Optional[MinimizationAttack] = None,\n",
" steps: int = 64,\n",
" initial_gradient_eval_steps: int = 100,\n",
" max_gradient_eval_steps: int = 10000,\n",
" stepsize_search: Union[\n",
" Literal[\"geometric_progression\"], Literal[\"grid_search\"]\n",
" ] = \"geometric_progression\",\n",
" gamma: float = 1.0,\n",
" tensorboard: Union[Literal[False], None, str] = False,\n",
" constraint: Union[Literal[\"linf\"], Literal[\"l2\"]] = \"l2\",\n",
"):\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2QWqjr-Y7R75"
},
"source": [
"#### Adversarial Attack Example\n",
"\n",
"* Attack method: HopSkipJump Attack\n",
"* Options: 10 iterations + start from a random target (untargeted attack)\n",
" * Each iteration includes a binary search step, gradient approximation step, and geometric progression step.\n",
"* Images: 1,000 test images"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 991
},
"id": "wnQGScAe7Q_1",
"outputId": "80a4b332-f27a-4049-9905-0aeab66d8278"
},
"source": [
"import time\n",
"import random\n",
"import foolbox as fb\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"model.eval()\n",
"start_time = time.time()\n",
"\n",
"fmodel = fb.PyTorchModel(model, bounds=(0, 1))\n",
"attack = fb.attacks.HopSkipJump(init_attack=None, steps=10,\n",
" initial_gradient_eval_steps=100, max_gradient_eval_steps=100)\n",
"\n",
"running_loss = 0.\n",
"running_corrects = 0\n",
"running_success = 0\n",
"running_length = 0\n",
"\n",
"running_l0 = 0\n",
"running_l2 = 0\n",
"running_mse = 0\n",
"running_linf = 0\n",
"\n",
"num_images = 1000\n",
"\n",
"for i, (inputs, labels) in enumerate(test_dataloader):\n",
" num_images -= labels.shape[0]\n",
"\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # generate \"random target instances\" that are correctly classified.\n",
" starting_points = torch.zeros(labels.shape[0], 3, 32, 32).to(device)\n",
" target_labels = torch.zeros(labels.shape[0]).long().to(device)\n",
" for k in range(labels.shape[0]):\n",
" starting_points[k], target_labels[k] = test_dataset[random.randint(0, len(test_dataset) - 1)]\n",
" outputs = model(starting_points)\n",
" _, preds = torch.max(outputs, 1)\n",
" while True:\n",
" condition = 0\n",
" idx = torch.tensor([], dtype=torch.long)\n",
" if target_labels.ne(preds).sum() > 0: # to be correctly classified\n",
" idx = torch.cat([idx, torch.arange(0, labels.shape[0]).long()[target_labels.ne(preds)]], dim=0)\n",
" condition += 1\n",
" if target_labels.eq(labels).sum() > 0: # to be different from the original labels\n",
" idx = torch.cat([idx, torch.arange(0, labels.shape[0]).long()[target_labels.eq(labels)]], dim=0)\n",
" condition += 1\n",
" idx = torch.unique(idx)\n",
" if condition == 0:\n",
" break\n",
" for k in list(idx):\n",
" starting_points[k], target_labels[k] = test_dataset[random.randint(0, len(test_dataset) - 1)]\n",
" outputs = model(starting_points[idx])\n",
" _, preds[idx] = torch.max(outputs, 1)\n",
"\n",
" attack_criterion = fb.criteria.Misclassification(labels)\n",
" _, adv_untargeted, _ = attack(fmodel, inputs, criterion=attack_criterion, starting_points=starting_points, epsilons=None) # adversarial attack\n",
"\n",
" outputs = model(adv_untargeted)\n",
" _, preds = torch.max(outputs, 1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" running_loss += loss.item()\n",
" running_corrects += torch.sum(preds == labels.data)\n",
" running_success += torch.sum(preds != labels.data)\n",
" running_length += labels.shape[0]\n",
"\n",
" l0, l2, mse, linf = get_distance(adv_untargeted, inputs)\n",
" running_l0 += l0.sum().item()\n",
" running_l2 += l2.sum().item()\n",
" running_mse += mse.sum().item()\n",
" running_linf += linf.sum().item()\n",
"\n",
" if i == 0:\n",
" # Display the first 4 images in the first batch.\n",
" print('The dimension of an image tensor:', inputs.shape[1:])\n",
" print('[Prediction Result Examples]')\n",
" images = torchvision.utils.make_grid(adv_untargeted[:4])\n",
" imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[:4]]) +\n",
" '\\npredicted labels: ' + str([int(x) for x in preds[:4]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[:4]):\n",
" print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[:4]):\n",
" print(f'Image #{j + 1}: {class_names[label]} ({label})')\n",
"\n",
" # Display the second 4 images in the first batch.\n",
" images = torchvision.utils.make_grid(adv_untargeted[4:8])\n",
" imshow_batch(images.cpu(), title='original labels: ' + str([int(x) for x in labels[4:8]]) +\n",
" '\\npredicted labels: ' + str([int(x) for x in preds[4:8]]))\n",
" print('Original labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(labels[4:8]):\n",
" print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
" print('Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>')\n",
" for j, label in enumerate(preds[4:8]):\n",
" print(f'Image #{j + 5}: {class_names[label]} ({label})')\n",
"\n",
" if i % 10 == 0:\n",
" cur_running_loss = running_loss / running_length\n",
" running_acc = running_corrects / running_length * 100.\n",
" running_asr = running_success / running_length * 100.\n",
" print('[Step #{}] Loss: {:.4f} Accuracy: {:.4f}% Attack success rate: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(i, cur_running_loss, running_acc, running_asr, time.time() - start_time, running_length))\n",
"\n",
" if num_images <= 0:\n",
" break\n",
"\n",
"epoch_loss = running_loss / running_length\n",
"epoch_acc = running_corrects / running_length * 100.\n",
"epoch_asr = running_success / running_length * 100.\n",
"print('[Validation] Loss: {:.4f} Accuracy: {:.4f}% Attack success rate: {:.4f}% Time elapsed: {:.4f}s (total {} images)'.format(epoch_loss, epoch_acc, epoch_asr, time.time() - start_time, running_length))\n",
"\n",
"print('[Size of Perturbation]')\n",
"print('Average L0 distance (the number of changed parameters):', running_l0 / running_length)\n",
"print('Average L2 distance:', running_l2 / running_length)\n",
"print('Average MSE:', running_mse / running_length)\n",
"print('Average Linf distance (the maximum changed values):', running_linf / running_length)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"The dimension of an image tensor: torch.Size([3, 32, 32])\n",
"[Prediction Result Examples]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: cat (3)\n",
"Image #2: bird (2)\n",
"Image #3: ship (8)\n",
"Image #4: bird (2)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #1: frog (6)\n",
"Image #2: deer (4)\n",
"Image #3: cat (3)\n",
"Image #4: airplane (0)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Original labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #5: airplane (0)\n",
"Image #6: dog (5)\n",
"Image #7: truck (9)\n",
"Image #8: bird (2)\n",
"Predicted labels >>>>>>>>>>>>>>>>>>>>>>>>>\n",
"Image #5: ship (8)\n",
"Image #6: bird (2)\n",
"Image #7: cat (3)\n",
"Image #8: dog (5)\n",
"[Step #0] Loss: 0.0133 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 46.6765s (total 64 images)\n",
"[Step #10] Loss: 0.0140 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 508.1112s (total 704 images)\n",
"[Validation] Loss: 0.0137 Accuracy: 0.0000% Attack success rate: 100.0000% Time elapsed: 738.7502s (total 1024 images)\n",
"[Size of Perturbation]\n",
"Average L0 distance (the number of changed parameters): 2954.9228515625\n",
"Average L2 distance: 2.029694177210331\n",
"Average MSE: 0.0015759490706841461\n",
"Average Linf distance (the maximum changed values): 0.10391072370111942\n"
],
"name": "stdout"
}
]
}
]
}