Adversarial Robustness

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

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.