RobustBench: a standardized adversarial robustness benchmark [NeurIPS 2021 Benchmarks and Datasets Track]

Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

EasyRobust: an Easy-to-use library for state-of-the-art Robust Computer Vision Research with PyTorch.

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

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training

[CVPR 2022] "Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations" by Tianlong Chen*, Peihao Wang*, Zhiwen Fan, Zhangyang Wang

Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs

Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples" & "Fixing Data Augmentation to Improve Adversarial Robustness" in PyTorch

[CVPR 2020] Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

[ICLR 2021] "InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective" by Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, Jingjing Liu

Feature Scattering Adversarial Training (NeurIPS19)

Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).

[NeurIPS'20 Oral] DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles

[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang

[ICML 2021] This is the official github repo for training L_inf dist nets with high certified accuracy.

Contains notebooks for the PAR tutorial at CVPR 2021.

Implementing the algorithm from our paper: "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning".

Code for FAB-attack