MMA-Diffusion: MultiModal Attack on Diffusion Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914735310503936 |
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| author | Yang, Yijun Gao, Ruiyuan Wang, Xiaosen Ho, Tsung-Yi Xu, Nan Xu, Qiang |
| author_facet | Yang, Yijun Gao, Ruiyuan Wang, Xiaosen Ho, Tsung-Yi Xu, Nan Xu, Qiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_17516 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MMA-Diffusion: MultiModal Attack on Diffusion Models Yang, Yijun Gao, Ruiyuan Wang, Xiaosen Ho, Tsung-Yi Xu, Nan Xu, Qiang Cryptography and Security Computer Vision and Pattern Recognition 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. |
| title | MMA-Diffusion: MultiModal Attack on Diffusion Models |
| topic | Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.17516 |