Title: Multimodal Information is All You Need for Adversarial Purification via Diffusion Models
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: adversarial purification, adversarial defense, stable diffusion models, prompt learning, multimodal learning

Abstract: Adversarial defense aims to find true semantic labels of adversarial examples, where diffusion-based adversarial purification as intriguing adversarial defense methods can restore data perturbed by unseen attacks to clean distribution without training classifiers. However, unimodal diffusion-based approaches rely on noise schedules to implicitly preserve labels, whereas recently proposed multimodal variants add textual control but require adversarial training and heavy distillation. Both approaches lack theoretical guarantees.
In this work, we propose MultiDAP that uses multimodal diffusion models for adversarial purification. MultiDAP first learn prompts from clean text-image pair data for clean image generation, where context tokens are numerical instead of text templates such as ``a photo of $\cdot$'' for rich contextual information and hence enhance adversarial robustness. Given learned prompts and adversarial examples, MultiDAP then purify inputs via minimizing regularized DDPM losses iteratively for only a few steps. Theoretical guarantees for two phases are also provided. In experiments, our proposed model achieve improvement of zero-shot adversarial defense performance over unimodal diffusion models and multimodal variants with text templates.