Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
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Updated
May 16, 2024 - Python
Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]
A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.
alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
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
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)
[NeurIPS'20 Oral] DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).
[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.
Implementing the algorithm from our paper: "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning".
Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
[ICLR 2022] "Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?" by Yonggan Fu, Shunyao Zhang, Shang Wu, Cheng Wan, Yingyan Lin
Fantastic Robustness Measures: The Secrets of Robust Generalization [NeurIPS 2023]
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