DiffDefense: Defending against Adversarial Attacks via Diffusion Models
Hondamunige Prasanna Silva Lorenzo Seidenari Alberto Del Bimbo
This paper presents a novel reconstruction method that leverages Diffusion
Models to protect machine learning classifiers against adversarial attacks, all
without requiring any modifications to the classifiers themselves. The
susceptibility of machine learning models to minor input perturbations renders
them vulnerable to adversarial attacks. While diffusion-based methods are
typically disregarded for adversarial defense due to their slow reverse
process, this paper demonstrates that our proposed method offers robustness
against adversarial threats while preserving clean accuracy, speed, and
plug-and-play compatibility. Code at:
https://github.com/HondamunigePrasannaSilva/DiffDefence.