Hui Luo

Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds.

This paper reveals that samples generated by a well-trained generative model are close to clean ones but far from adversarial ones, and proposes Consistency Model-based Adversarial Purification (CMAP), which optimizes vectors within the latent space of a pre-trained consistency model to generate samples for restoring clean data.