Summary of Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances, by Shilin Lu et al.
Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances
by Shilin Lu, Zihan Zhou, Jiayou Lu, Yuanzhi Zhu, Adams Wai-Kin Kong
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 presents a comprehensive benchmark, W-Bench, to evaluate the robustness of image watermarking methods against various image editing techniques. The current methods are vulnerable to advanced editing techniques enabled by large-scale text-to-image models, which can distort embedded watermarks. Eleven representative watermarking methods were evaluated against prevalent editing techniques, demonstrating that most fail to detect watermarks after edits. To address this limitation, the authors propose VINE, a watermarking method that enhances robustness while maintaining high image quality. This is achieved by analyzing frequency characteristics of image editing and leveraging a pretrained diffusion model for watermark embedding. Experimental results show that VINE outperforms existing methods in both image quality and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to solve the problem of image watermarking being vulnerable to advanced image editing techniques. Current methods can’t detect watermarks after edits, which is a challenge for copyright protection. The authors created a new benchmark called W-Bench that tests how well different methods work against various editing techniques. They found that most methods don’t work well and propose a new method called VINE that does better. This method uses frequency characteristics of image editing and a special model to embed watermarks in a way that’s hard to distort. The results show that VINE is the best method so far. |
Keywords
» Artificial intelligence » Diffusion model » Embedding
