MMA-Diffusion: MultiModal Attack on Diffusion Models

• URL: http://arxiv.org/abs/2311.17516v4
• Date: Sat, 30 Mar 2024 08:35:17 GMT
• Title: MMA-Diffusion: MultiModal Attack on Diffusion Models
• Authors: Yijun Yang, Ruiyuan Gao, Xiaosen Wang, Tsung-Yi Ho, Nan Xu, Qiang Xu,
• Abstract summary: MMA-Diffusion presents a significant and realistic threat to the security of T2I models. It circumvents current defensive measures in both open-source models and commercial online services.
• Score: 32.67807098568781
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, Text-to-Image (T2I) models have seen remarkable advancements, gaining widespread adoption. However, this progress has inadvertently opened avenues for potential misuse, particularly in generating inappropriate or Not-Safe-For-Work (NSFW) content. Our work introduces MMA-Diffusion, a framework that presents a significant and realistic threat to the security of T2I models by effectively circumventing current defensive measures in both open-source models and commercial online services. Unlike previous approaches, MMA-Diffusion leverages both textual and visual modalities to bypass safeguards like prompt filters and post-hoc safety checkers, thus exposing and highlighting the vulnerabilities in existing defense mechanisms.

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