Towards Better Adversarial Purification via Adversarial Denoising Diffusion Training

Recently, diffusion-based purification (DBP) has emerged as a promising approach for defending against adversarial attacks. However, previous studies have used questionable methods to evaluate the robustness of DBP models, their explanations of DBP robustness also lack experimental support. We re-examine DBP robustness using precise gradient, and discuss the impact of stochasticity on DBP robustness. To better explain DBP robustness, we assess DBP robustness under a novel attack setting, Deterministic White-box, and pinpoint stochasticity as the main factor in DBP robustness. Our results suggest that DBP models rely on stochasticity to evade the most effective attack direction, rather than directly countering adversarial perturbations. To improve the robustness of DBP models, we propose Adversarial Denoising Diffusion Training (ADDT). This technique uses Classifier-Guided Perturbation Optimization (CGPO) to generate adversarial perturbation through guidance from a pre-trained classifier, and uses Rank-Based Gaussian Mapping (RBGM) to convert adversarial pertubation into a normal Gaussian distribution. Empirical results show that ADDT improves the robustness of DBP models. Further experiments confirm that ADDT equips DBP models with the ability to directly counter adversarial perturbations.