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}.

采用多模态先验信息，我们提出了一种名为MMP-Attack的目标攻击方法，它可以在图像内容中添加目标对象并同时删除原始对象。MMP-Attack相对于现有方法具有更好的普适性和可迁移性，可以有效地攻击商业文本到图像模型（如DALL-E 3），这是对商业文本到图像模型进行迁移式攻击的首次成功尝试。

作弊的后缀: 基于多模态先验的针对文本到图像扩散模型的定向攻击