MMA-Diffusion: MultiModal Attack on Diffusion Models
Yijun Yang, Ruiyuan Gao, Xiaosen Wang, Tsung-Yi Ho, Nan Xu, Qiang Xu
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. Our codes are available at https://github.com/cure-lab/MMA-Diffusion.
BibTeX
@inproceedings{cvpr2024_mmadiffusionmult,
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},
booktitle = {CVPR 2024},
year = {2024}
}