adversarial-robustness topic
ares
A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.
auto-attack
Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
self-adaptive-training
[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training
robustbench
RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]
InfoBERT
[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
denoised-smoothing
Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
alpha-beta-CROWN
alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
Adv-SS-Pretraining
[CVPR 2020] Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
square-attack
Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]
FeatureScatter
Feature Scattering Adversarial Training (NeurIPS19)