torchattacks 2.9

Adversarial Attacks for PyTorch

Adversarial-Attacks-Pytorch

This is a lightweight repository of adversarial attacks for Pytorch.

Torchattacks is a PyTorch library that contains adversarial attacks to generate adversarial examples and to verify the robustness of deep learning models.

Clean Image	Adversarial Image

Table of Contents

1. Usage
2. Attacks and Papers
3. Documentation
4. Expanding the Usage
5. Contribution
6. Recommended Sites and Packages

Usage

:clipboard: Dependencies

• torch 1.2.0
• python 3.6

:hammer: Installation

• pip install torchattacks or
• git clone https://github.com/Harry24k/adversairal-attacks-pytorch

import torchattacks
atk = torchattacks.PGD(model, eps=8/255, alpha=2/255, steps=4)
adversarial_images = atk(images, labels)

:warning: Precautions

• All images should be scaled to [0, 1] with transform[to.Tensor()] before used in attacks. To make it easy to use adversarial attacks, a reverse-normalization is not included in the attack process. To apply an input normalization, please add a normalization layer to the model. Please refer to the demo.

• All models should return ONLY ONE vector of (N, C) where C = number of classes. Considering most models in torchvision.models return one vector of (N,C), where N is the number of inputs and C is thenumber of classes, torchattacks also only supports limited forms of output. Please check the shape of the model’s output carefully.

• torch.backends.cudnn.deterministic = True to get same adversarial examples with fixed random seed. Some operations are non-deterministic with float tensors on GPU [discuss]. If you want to get same results with same inputs, please run torch.backends.cudnn.deterministic = True[ref].

Attacks and Papers

Implemented adversarial attacks in the papers.

The distance measure in parentheses.

• Explaining and harnessing adversarial examples (Dec 2014): Paper

  • FGSM (Linf)

• DeepFool: a simple and accurate method to fool deep neural networks (Nov 2015): Paper

  • DeepFool (L2)

• Adversarial Examples in the Physical World (Jul 2016): Paper

  • BIM or iterative-FSGM (Linf)

• Towards Evaluating the Robustness of Neural Networks (Aug 2016): Paper

  • CW (L2)

• Ensemble Adversarial Traning: Attacks and Defences (May 2017): Paper

  • RFGSM (Linf)

• Towards Deep Learning Models Resistant to Adversarial Attacks (Jun 2017): Paper

  • PGD (Linf)

• Boosting Adversarial Attacks with Momentum (Oct 2017): Paper

  • MIFGSM (Linf) - :heart_eyes: Contributor zhuangzi926

• Theoretically Principled Trade-off between Robustness and Accuracy (Jan 2019): Paper

  • TPGD (Linf)

• Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network" (Jul 2019): Paper

  • APGD or EOT + PGD (Linf)

• Fast is better than free: Revisiting adversarial training (Jan 2020): Paper

  • FFGSM (Linf)

Documentation

:book: ReadTheDocs

Here is a documentation for this package.

:bell: ​Citation

If you want to cite this package, please use the following BibTex:

@article{kim2020torchattacks,
  title={Torchattacks: A Pytorch Repository for Adversarial Attacks},
  author={Kim, Hoki},
  journal={arXiv preprint arXiv:2010.01950},
  year={2020}
}

:rocket: Demos

• White Box Attack with ImageNet (code, nbviewer): Using torchattacks to make adversarial examples with the ImageNet dataset to fool Inception v3.
• Black Box Attack with CIFAR10 (code, nbviewer): This demo provides an example of black box attack with two different models. First, make adversarial datasets from a holdout model with CIFAR10 and save it as torch dataset. Second, use the adversarial datasets to attack a target model.
• Adversairal Training with MNIST (code, nbviewer): This code shows how to do adversarial training with this repository. The MNIST dataset and a custom model are used in this code. The adversarial training is performed with PGD, and then FGSM is applied to evaluate the model.
• Applications of MultiAttack with CIFAR10 (code, nbviewer): This code shows the applications of Multiattack. It can be used for implementing (1) Attack with random restarts, and (2) Attack on only correct examples.

Expanding the Usage

Torchattacks supports collaboration with other attack packages.

Through expending the usage, we can use fucntions in torchattacks such as save, multiattack.

:milky_way: AutoAttack

• https://github.com/fra31/auto-attack
• pip install git+https://github.com/fra31/auto-attack

from torchattacks.attack import Attack
import autoattack

class AutoAttack(Attack):
    def __init__(self, model, eps):
        super(AutoAttack, self).__init__("AutoAttack", model)
        self.adversary = autoattack.AutoAttack(self.model, norm='Linf',
                                               eps=eps, version='standard', verbose=False)
        self._attack_mode = 'only_default'

    def forward(self, images, labels):
        adv_images = self.adversary.run_standard_evaluation(images.cuda(), labels.cuda(),
                                                            bs=images.shape[0])
        return adv_images

atk = AutoAttack(model, eps=0.3)
atk.save(data_loader=test_loader, file_name="_temp.pt", accuracy=True)

:milky_way: FoolBox

• https://github.com/bethgelab/foolbox
• pip install foolbox
• e.g., L2BrendelBethge

from torchattacks.attack import Attack
import foolbox as fb

class L2BrendelBethge(Attack):
    def __init__(self, model):
        super(L2BrendelBethge, self).__init__("L2BrendelBethge", model)
        self.fmodel = fb.PyTorchModel(self.model, bounds=(0,1), device=self.device)
        self.init_attack = fb.attacks.DatasetAttack()
        self.adversary = fb.attacks.L2BrendelBethgeAttack(init_attack=self.init_attack)
        self._attack_mode = 'only_default'

    def forward(self, images, labels):
        images, labels = images.to(self.device), labels.to(self.device)

        # DatasetAttack
        batch_size = len(images)
        batches = [(images[:batch_size//2], labels[:batch_size//2]),
                   (images[batch_size//2:], labels[batch_size//2:])]
        self.init_attack.feed(model=self.fmodel, inputs=batches[0][0]) # feed 1st batch of inputs
        self.init_attack.feed(model=self.fmodel, inputs=batches[1][0]) # feed 2nd batch of inputs
        criterion = fb.Misclassification(labels)
        init_advs = self.init_attack.run(self.fmodel, images, criterion)

        # L2BrendelBethge
        adv_images = self.adversary.run(self.fmodel, images, labels, starting_points=init_advs)
        return adv_images

atk = L2BrendelBethge(model)
atk.save(data_loader=test_loader, file_name="_temp.pt", accuracy=True)

:milky_way: Adversarial-Robustness-Toolbox (ART)

• https://github.com/IBM/adversarial-robustness-toolbox
• git clone https://github.com/IBM/adversarial-robustness-toolbox
• e.g., SaliencyMapMethod (or Jacobian based saliency map attack)

import torch.nn as nn
import torch.optim as optim

from torchattacks.attack import Attack

import art.attacks.evasion as evasion
from art.classifiers import PyTorchClassifier

class JSMA(Attack):
    def __init__(self, model, theta=1/255, gamma=0.15, batch_size=128):
        super(JSMA, self).__init__("JSMA", model)
        self.classifier = PyTorchClassifier(
                            model=self.model, clip_values=(0, 1),
                            loss=nn.CrossEntropyLoss(),
                            optimizer=optim.Adam(self.model.parameters(), lr=0.01),
                            input_shape=(1, 28, 28), nb_classes=10)
        self.adversary = evasion.SaliencyMapMethod(classifier=self.classifier,
                                                   theta=theta, gamma=gamma,
                                                   batch_size=batch_size)
        self.target_map_function = lambda labels: (labels+1)%10
        self._attack_mode = 'only_default'

    def forward(self, images, labels):
        adv_images = self.adversary.generate(images, self.target_map_function(labels))
        return torch.tensor(adv_images).to(self.device)

atk = JSMA(model)
atk.save(data_loader=test_loader, file_name="_temp.pt", accuracy=True)

Contribution

Contribution is always welcome! Use pull requests :blush:

Recommended Sites and Packages

• Adversarial Attack Packages:

  • https://github.com/IBM/adversarial-robustness-toolbox: Adversarial attack and defense package made by IBM. TensorFlow, Keras, Pyotrch available.
  • https://github.com/bethgelab/foolbox: Adversarial attack package made by Bethge Lab. TensorFlow, Pyotrch available.
  • https://github.com/tensorflow/cleverhans: Adversarial attack package made by Google Brain. TensorFlow available.
  • https://github.com/BorealisAI/advertorch: Adversarial attack package made by BorealisAI. Pytorch available.
  • https://github.com/DSE-MSU/DeepRobust: Adversarial attack (especially on GNN) package made by BorealisAI. Pytorch available.
  • https://github.com/fra31/auto-attack: Set of attacks that is believed to be the strongest in existence. TensorFlow, Pyotrch available.

• Adversarial Defense Leaderboard:

  • https://github.com/MadryLab/mnist_challenge
  • https://github.com/MadryLab/cifar10_challenge
  • https://www.robust-ml.org/
  • https://robust.vision/benchmark/leaderboard/
  • https://github.com/RobustBench/robustbench
  • https://github.com/Harry24k/adversarial-defenses-pytorch

• Adversarial Attack and Defense Papers:

  • https://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html: A Complete List of All (arXiv) Adversarial Example Papers made by Nicholas Carlini.
  • https://github.com/chawins/Adversarial-Examples-Reading-List: Adversarial Examples Reading List made by Chawin Sitawarin.