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：随着扩散模型生成的AI内容被广泛使用，有关虚假信息和版权侵权的担忧日益加剧。水印技术是识别这些AI生成图像并防止其滥用的关键手段。本文提出了一种名为Shallow Diffuse的新型水印技术，能够在扩散模型输出中嵌入鲁棒且不可见的水印。与现有方法将水印嵌入整合到整个扩散采样过程不同，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，从而有效地将其与图像生成过程分离。我们的理论与实证分析表明，这种解耦策略显著提升了数据生成的一致性与水印的可检测性。大量实验进一步验证了Shallow Diffuse在鲁棒性和一致性方面优于现有水印方法。相关代码已发布于https://github.com/liwd190019/Shallow-Diffuse。