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.

翻译：近年来，文本到图像（Text-to-Image, T2I）模型取得了显著进展，并得到广泛应用。然而，这一进步也无意中为潜在滥用行为敞开了大门，尤其是在生成不适当或不宜工作场合（Not-Safe-For-Work, NSFW）的内容方面。我们的工作提出了MMA-Diffusion框架，该框架通过有效规避开源模型及商业在线服务中现有的防御措施，对T2I模型的安全性构成了重大且现实的威胁。与以往方法不同，MMA-Diffusion同时利用文本和视觉模态来绕过提示过滤器（prompt filters）及事后安全检查器（post-hoc safety checkers）等安全机制，从而揭示并凸显了现有防御体系中的薄弱环节。