Cheating Suffix: Targeted Attack to Text-To-Image Diffusion Models with Multi-Modal Priors
<|reference_start|>Cheating Suffix: Targeted Attack to Text-To-Image Diffusion Models with Multi-Modal Priors: Diffusion models have been widely deployed in various image generation tasks, demonstrating an extraordinary connection between image and text modalities. However, they face challenges of being maliciously exploited to generate harmful or sensitive images by appending a specific suffix to the original prompt. Existing works mainly focus on using single-modal information to conduct attacks, which fails to utilize multi-modal features and results in less than satisfactory performance. Integrating multi-modal priors (MMP), i.e. both text and image features, we propose a targeted attack method named MMP-Attack in this work. Specifically, the goal of MMP-Attack is to add a target object into the image content while simultaneously removing the original object. The MMP-Attack shows a notable advantage over existing works with superior universality and transferability, which can effectively attack commercial text-to-image (T2I) models such as DALL-E 3. To the best of our knowledge, this marks the first successful attempt of transfer-based attack to commercial T2I models. Our code is publicly available at \url{https://github.com/ydc123/MMP-Attack}.<|reference_end|>
@article{yang2024cheating,
    title={Cheating Suffix: Targeted Attack to Text-To-Image Diffusion Models with
  Multi-Modal Priors},
    author={Dingcheng Yang, Yang Bai, Xiaojun Jia, Yang Liu, Xiaochun Cao, Wenjian
  Yu},
    journal={arXiv preprint arXiv:2402.01369},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01369},
    primaryClass={cs.LG cs.CR cs.CV}
}