Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

A pytorch adversarial library for attack and defense methods on images and graphs

🗣️ Tool to generate adversarial text examples and test machine learning models against them

Implementation of Papers on Adversarial Examples

auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs

alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, 2023, and 2024)

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models (published in ICLR2018)

DEEPSEC: A Uniform Platform for Security Analysis of Deep Learning Model

Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training.

Library containing PyTorch implementations of various adversarial attacks and resources

PyTorch library for adversarial attack and training

[CVPR 2020] When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

Revisiting Transferable Adversarial Images (arXiv)

Code for "Detecting Adversarial Samples from Artifacts" (Feinman et al., 2017)

Code for our CVPR 2018 paper, "On the Robustness of Semantic Segmentation Models to Adversarial Attacks"

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

Understanding and Improving Fast Adversarial Training [NeurIPS 2020]

Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).