Summary of Hidden in the Noise: Two-stage Robust Watermarking For Images, by Kasra Arabi et al.
Hidden in the Noise: Two-Stage Robust Watermarking for Images
by Kasra Arabi, Benjamin Feuer, R. Teal Witter, Chinmay Hegde, Niv Cohen
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 abstract proposes a novel approach to image watermarking that tackles the limitations of current state-of-the-art methods. Image generators like deepfakes are becoming increasingly prevalent, sparking societal concerns about AI-generated content. To mitigate these concerns, responsible model owners can embed watermarks into their generated images. However, existing methods remain vulnerable to forgery and removal attacks due to the distortion caused by watermarking techniques on the distribution of generated images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image watermarking is a way to identify AI-generated content and make sure it’s not used in unauthorized ways. Right now, this technology isn’t very good because watermarks can be easily removed or fake. The problem is that adding watermarks changes how the generated images look, which gives away secrets about how the watermark was added. This paper tries to solve this problem by coming up with a new way to add watermarks that doesn’t have these weaknesses. |
