torchattacks 2.12.3

Adversarial Attacks for PyTorch

Adversarial-Attacks-PyTorch

[README [KOR]](https://github.com/Harry24k/adversairal-attacks-pytorch/blob/master/README_KOR.md)

Torchattacks is a PyTorch library that contains adversarial attacks to generate adversarial examples.

Clean Image	Adversarial Image

Table of Contents

1. Usage
2. Attacks and Papers
3. Performance Comparison
4. Documentation
5. Citation
6. Expanding the Usage
7. Contribution
8. Recommended Sites and Packages

Usage

:clipboard: Dependencies

• torch==1.4.0
• 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. In the case of the model returns multiple outputs, please refer to the demo.

• 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.

Name	Paper	Remark
FGSM (Linf)	Explaining and harnessing adversarial examples (Goodfellow et al., 2014)
BIM (Linf)	Adversarial Examples in the Physical World (Kurakin et al., 2016)	Basic iterative method or Iterative-FSGM
CW (L2)	Towards Evaluating the Robustness of Neural Networks (Carlini et al., 2016)
RFGSM (Linf)	Ensemble Adversarial Traning: Attacks and Defences (Tramèr et al., 2017)	Random initialization + FGSM
PGD (Linf)	Towards Deep Learning Models Resistant to Adversarial Attacks (Mardry et al., 2017)	Projected Gradient Method
PGDL2 (L2)	Towards Deep Learning Models Resistant to Adversarial Attacks (Mardry et al., 2017)	Projected Gradient Method
MIFGSM (Linf)	Boosting Adversarial Attacks with Momentum (Dong et al., 2017)	:heart_eyes: Contributor zhuangzi926, huitailangyz
TPGD (Linf)	Theoretically Principled Trade-off between Robustness and Accuracy (Zhang et al., 2019)
APGD (Linf)	Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network" (Zimmermann, 2019)	EOT + PGD
FFGSM (Linf)	Fast is better than free: Revisiting adversarial training (Wong et al., 2020)	Random initialization + FGSM

Performance Comparison

For a fair comparison, Robustbench is used. As for the comparison packages, currently updated and the most cited methods were selected:

• Foolbox: 178 citations and last update 2020.10.19.

• ART: 102 citations and last update 2020.12.11.

Robust accuracy against each attack and elapsed time on the first 50 images of CIFAR10. For L2 attacks, the average L2 distances between adversarial images and the original images are recorded. The code is here (code, nbviewer). All experiments were done on GeForce RTX 2080.

Attack (Linf)	Package	Wong2020	Rice2020	Carmon2019	Remark
FGSM	Torchattacks	48% (15 ms)	62% (94 ms)	68% (18 ms)
	Foolbox*	48% (34 ms)	62% (46 ms)	68% (27 ms)
	ART	48% (50 ms)	62% (799 ms)	68% (221 ms)
PGD	Torchattacks	44% (156 ms)	58% (2770 ms)	58% (692 ms)
	Foolbox*	44% (249 ms)	58% (3452 ms)	58% (945 ms)
	ART	44% (495 ms)	58% (4782 ms)	58% (1444 ms)
Attack (L2)	Package	Wong2020	Rice2020	Carmon2019	Remark
CW†	Torchattacks	14% / 0.61 (4367 ms)	22% / 0.56 (43680 ms)	26% / 0.48 (13032 ms)	Highest Success Rate
	Foolbox*	32% / 0.41 (4530 ms)	34% / 0.43 (45273 ms)	32% / 0.42 (13314 ms)	Smallest Perturbation
	ART	24% / 0.71 (71613 ms)	26% / 0.65 (691977 ms)	26% / 0.62 (206250 ms)
PGDL2	Torchattacks	68% / 0.5 (166 ms)	70% / 0.5 (2796 ms)	68% / 0.5 (712 ms)
	Foolbox*	68% / 0.5 (267 ms)	70% / 0.5 (3501 ms)	68% / 0.5 (962 ms)
	ART	68% / 0.5 (470 ms)	70% / 0.5 (4822 ms)	68% / 0.5 (1441 ms)

^*Note that Foolbox returns accuracy and adversarial images simultaneously, thus the actual time for generating adversarial images might be shorter than the records.

^†Considering that the binary search algorithm for const c can be time-consuming, torchattacks supports customized grid search as in code, nbviewer.

Documentation

:book: ReadTheDocs

Here is a documentation for this package.

:mag_right: Update Records

Here is update records of this package.

: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.

Citation

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

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

Expanding the Usage

Torchattacks supports collaboration with other attack packages.

Through expending the usage, we can use fucntions in torchattacks such as save and 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, save_path="_temp.pt", verbose=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, save_path="_temp.pt", verbose=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, save_path="_temp.pt", verbose=True)

Contribution

All kind of contributions are always welcome! :blush:

If you are interested in adding a new attack to this repo or fixing some issues, please have a look at contribution.md.

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.