torchattacks 2.4

Adversarial Attacks for PyTorch

Adversarial-Attacks-Pytorch

This is a lightweight repository of adversarial attacks for Pytorch.

There are popular attack methods and some utils.

Here is a documentation for this package.

Table of Contents

1. Usage
2. Attacks and Papers
3. Demos
4. Frequently Asked Questions
5. Update Records
6. Recommended Sites and Packages

Usage

Dependencies

• torch 1.2.0
• python 3.6

Installation

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

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

Precautions

• WARNING :: All images should be scaled to [0, 1] with transform[to.Tensor()] before used in attacks.
• WARNING :: All models should return ONLY ONE vector of (N, C) where C = number of classes.

Attacks and Papers

The papers and the methods with a brief summary and example. All attacks in this repository are provided as CLASS. If you want to get attacks built in Function, please refer below repositories.

• Explaining and harnessing adversarial examples : Paper, Repo

• FGSM

• DeepFool: a simple and accurate method to fool deep neural networks : Paper

• DeepFool

• Adversarial Examples in the Physical World : Paper, Repo

  • BIM or iterative-FSGM
  • StepLL

• Towards Evaluating the Robustness of Neural Networks : Paper, Repo

• CW(L2)

• Ensemble Adversarial Traning : Attacks and Defences : Paper, Repo

• RFGSM

• Towards Deep Learning Models Resistant to Adversarial Attacks : Paper, Repo

• PGD(Linf)

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

  • APGD(EOT + PGD)

• Fast is better than free: Revisiting adversarial training" : Paper

  • FFGSM(Fast's FGSM)

• Theoretically Principled Trade-off between Robustness and Accuracy" : Paper

  • TPGD(TRADES' PGD)

Attack	Clean	Adversarial
FGSM
BIM
StepLL
RFGSM
CW
PGD(w/o random starts)
PGD(w/ random starts)
DeepFool

Demos

• White Box Attack with Imagenet (code): To make adversarial examples with the Imagenet dataset to fool Inception v3. However, the Imagenet dataset is too large, so only 'Giant Panda' is used.
• Black Box Attack with CIFAR10 (code): 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): 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 test the model.
• Targeted PGD with Imagenet (code): It shows we can perturb images to be classified into the labels we want with targeted PGD.
• MultiAttack with MNIST (code): This code shows an example of PGD with N-random-restarts.

Frequently Asked Questions

• I want to use image normalization. : In this case, you have to put normalize layer in the model. Please refer to DEMO:White Box Attack with Imagenet.
• There is no randomize process in my model, but attacks return different results. : Some operations are non-deterministic with float tensors on GPU. If you want to get same results with same inputs, please run "torch.backends.cudnn.deterministic = True".

Update Records

~Version 1.2 (DON'T USE)

• Pip packages were corrupted by accumulating previous versions.

Version 1.3

• Pip Package Re-uploaded.

Version 1.4

• PGD :
  • Now it supports targeted mode.

Version 1.5

• MultiAttack :
  • MultiAttack added.
  • With it, you can use PGD with N-random-restarts or stronger attacks with different methods.

Version 2.4 (Stable)

• steps instead of iters :

  • For compatibility reasons, all iters are changed to steps.

• FFGSM :

  • New FGSM proposed by Eric Wong et al. added.

• TPGD :

  • PGD(Linf) based on KL-Divergence loss proposed by Hongyang Zhang et al. added.

Recommended Sites and Packages

• Other 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.

• 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/

• 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.