DiffDefense: Defending against Adversarial Attacks via Diffusion Models

• URL: http://arxiv.org/abs/2309.03702v1
• Date: Thu, 7 Sep 2023 13:28:36 GMT
• Title: DiffDefense: Defending against Adversarial Attacks via Diffusion Models
• Authors: Hondamunige Prasanna Silva, Lorenzo Seidenari, and Alberto Del Bimbo
• Abstract summary: The susceptibility of machine learning models to perturbations renders them vulnerable to adversarial attacks. Our proposed method offers robustness against adversarial threats while preserving clean accuracy, speed, and plug-and-play compatibility.
• Score: 24.328384566645738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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