Adversarial Robustness

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

Improving the Efficiency of Self-Supervised Adversarial Training through Latent Clustering-based Selection

We introduce a Latent Clustering-based Selection method to choose a core subset from the entire unlabeled dataset, aiming to improve the efficiency of self-supervised adversarial training while preserving robustness.

Somrita Ghosh, Yuelin Xu, Xiao Zhang