Summary of Shallow Diffuse: Robust and Invisible Watermarking Through Low-dimensional Subspaces in Diffusion Models, by Wenda Li et al.
Shallow Diffuse: Robust and Invisible Watermarking through Low-Dimensional Subspaces in Diffusion Models
by Wenda Li, Huijie Zhang, Qing Qu
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Shallow Diffuse, a new watermarking technique that embeds robust and invisible watermarks into diffusion model outputs. This decouples the watermarking process from the image generation process, leveraging the presence of a low-dimensional subspace in the image generation process. The method ensures a substantial portion of the watermark lies in the null space of this subspace, making it detectable while preventing its integration into the generated images. Experimental results demonstrate that Shallow Diffuse outperforms existing methods in terms of robustness and consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better AI-generated content by adding a special code to identify where the image came from. It’s like putting a fingerprint on every picture made with AI, so we can tell what’s real and what’s fake. The new technique makes sure this code is hidden in a way that’s hard to detect, while still being able to find it if needed. |
Keywords
» Artificial intelligence » Diffusion model » Image generation
