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了广泛应用。然而，这一进步也不可避免地为潜在滥用打开了通道，特别是在生成不当内容或不适宜工作场所（Not-Safe-For-Work, NSFW）内容方面。本文提出MMA-Diffusion框架，通过对开源模型和商业在线服务有效规避当前防御措施，对T2I模型的安全性构成了显著且现实的威胁。与以往方法不同，MMA-Diffusion利用文本和视觉两种模态绕过提示词过滤器及事后安全检查器等防护机制，从而揭露并凸显了现有防御体系中的脆弱性。