adversarial-robustness topic

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

VITA-Group

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

imrahulr

Pytorch implementation of our NeurIPS'20 *Oral* paper "DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles".

zjysteven

Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off

imrahulr

Adversarial Attack and Defense in Deep Ranking, T-PAMI, 2024

cdluminate

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

VITA-Group

Code for FAB-attack

fra31

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

alibaba

[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“

VITA-Group

LAFEAT: Piercing Through Adversarial Defenses with Latent Features (CVPR 2021 Oral)

lafeat