Defending Against Frequency-Based Attacks with Diffusion Models

Abstract

Adversarial training is a common strategy for enhancing model robustness against adversarial attacks. However, it is typically tailored to the specific attack types it is trained on, limiting its ability to generalize to unseen threat models. Adversarial purification offers an alternative by leveraging a generative model to remove perturbations before classification. Since the purifier is trained independently of both the classifier and the threat models, it is better equipped to handle previously unseen attack scenarios. Diffusion models have proven highly effective for noise purification, not only in countering pixel-wise adversarial perturbations but also in addressing non-adversarial data shifts. In this study, we broaden the focus beyond pixel-wise robustness to explore the extent to which purification can mitigate both spectral and spatial adversarial attacks. Our findings highlight its effectiveness in handling diverse distortion patterns across low- to high-frequency regions.