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.
@article{arxiv.2311.17516,
title = {MMA-Diffusion: MultiModal Attack on Diffusion Models},
author = {Yijun Yang and Ruiyuan Gao and Xiaosen Wang and Tsung-Yi Ho and Nan Xu and Qiang Xu},
journal= {arXiv preprint arXiv:2311.17516},
year = {2024}
}
Comments
CVPR 2024. Our codes and benchmarks are available at https://github.com/cure-lab/MMA-Diffusion