Summary of Attack-resilient Image Watermarking Using Stable Diffusion, by Lijun Zhang et al.
Attack-Resilient Image Watermarking Using Stable Diffusion
by Lijun Zhang, Xiao Liu, Antoni Viros Martin, Cindy Xiong Bearfield, Yuriy Brun, Hui Guan
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, the authors propose a novel approach to image watermarking called ZoDiac. With the rise of generative models like stable diffusion, it has become crucial to track image provenance and ownership. The existing methods for injecting watermarks into images can be removed using these same models. To address this issue, the authors use a pre-trained stable diffusion model to inject a watermark into the latent space, making it possible to detect watermarks even when attacked. They evaluate ZoDiac on three benchmarks (MS-COCO, DiffusionDB, and WikiArt) and find that it outperforms state-of-the-art watermarking methods with a detection rate above 98% and a false positive rate below 6.4%. The authors hypothesize that the denoising process in diffusion models enhances the robustness of watermarks against strong attacks and validate this hypothesis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to add marks to images called ZoDiac. This is important because fake images can look real, and we need to be able to tell if an image is real or not. The problem is that methods used to put these marks in can be removed by the same technology that makes fake images. To solve this, they used a special kind of computer program called stable diffusion to add the mark to the invisible part of the image. They tested it on three sets of pictures and found that their method works really well, with almost no mistakes. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Latent space
