Adversarial Robustness

Efficient Semi-Supervised Adversarial Training via Latent Clustering-Based Data Reduction

We introduce latent clustering-based data reduction methods to choose a core subset from the entire unlabeled dataset, aiming to improve the efficiency of semi-supervised adversarial training while preserving robustness.

Somrita Ghosh, Yuelin Xu, Xiao Zhang

DiffPAD: Denoising Diffusion-based Adversarial Patch Decontamination

We propose DiffPAD, a novel framework that harnesses the power of diffusion models for adversarial patch decontamination.

Jia Fu, Xiao Zhang, Sepideh Pashami, Fatemeh Rahimian, Anders Holst