The widespread use of AI-generated content from diffusion models has raised significant concerns regarding misinformation and copyright infringement. Watermarking is a crucial technique for identifying these AI-generated images and preventing their misuse. In this paper, we introduce Shallow Diffuse, a new watermarking technique that embeds robust and invisible watermarks into diffusion model outputs. Unlike existing approaches that integrate watermarking throughout the entire diffusion sampling process, Shallow Diffuse decouples these steps by leveraging the presence of a low-dimensional subspace in the image generation process. This method ensures that a substantial portion of the watermark lies in the null space of this subspace, effectively separating it from the image generation process. Our theoretical and empirical analyses show that this decoupling strategy greatly enhances the consistency of data generation and the detectability of the watermark. Extensive experiments further validate that our Shallow Diffuse outperforms existing watermarking methods in terms of robustness and consistency. The codes are released at https://github.com/liwd190019/Shallow-Diffuse.

翻译：扩散模型生成的AI内容广泛使用，引发了关于虚假信息和版权侵权的重大担忧。水印技术是识别这些AI生成图像并防止其滥用的关键技术。本文提出Shallow Diffuse，一种将鲁棒且不可见水印嵌入扩散模型输出的新型水印技术。与现有方法在整个扩散采样过程中集成水印不同，Shallow Diffuse通过利用图像生成过程中存在的低维子空间，将这两个步骤解耦。该方法确保水印的绝大部分位于该子空间的零空间中，从而有效将其与图像生成过程分离。我们的理论和实证分析表明，这种解耦策略显著提升了数据生成的一致性和水印的可检测性。大量实验进一步验证了我们的Shallow Diffuse在鲁棒性和一致性方面优于现有水印方法。代码发布于https://github.com/liwd190019/Shallow-Diffuse。