Summary of Metr: Image Watermarking with Large Number Of Unique Messages, by Alexander Varlamov et al.
METR: Image Watermarking with Large Number of Unique Messages
by Alexander Varlamov, Daria Diatlova, Egor Spirin
First submitted to arxiv on: 15 Aug 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 proposed METR algorithm tackles the challenges of watermarking generative art, enabling clear identification of creators. By building upon Tree-Ring, a technique that encodes multiple messages without compromising attack resilience or image quality, METR ensures suitability for any Diffusion Model. The enhanced version, METR++, injects virtually unlimited unique messages while preserving image quality and demonstrating robustness to attacks. This approach has great potential for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Watermarking generative art makes it possible to identify creators. A new algorithm called METR helps solve this problem. It uses a technique called Tree-Ring, which lets it encode many different messages without hurting the image or making it easy to remove. METR works with any kind of diffusion model. To make it even better, an upgraded version called METR++ was created. It can add a huge number of unique messages while keeping the image quality good and making it hard for attackers to remove. |
Keywords
* Artificial intelligence * Diffusion model
