Summary of Finding Needles in a Haystack: a Black-box Approach to Invisible Watermark Detection, by Minzhou Pan et al.
Finding needles in a haystack: A Black-Box Approach to Invisible Watermark Detection
by Minzhou Pan, Zhenting Wang, Xin Dong, Vikash Sehwag, Lingjuan Lyu, Xue Lin
First submitted to arxiv on: 23 Mar 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 A novel method for detecting invisible watermarks is proposed in this paper, which can identify arbitrary watermarks within a reference dataset without relying on specific decoding methods or prior knowledge of the watermarking techniques. The WaterMark Detection (WMD) approach uses foundations of offset learning to isolate the influence of only watermarked samples in the reference dataset, allowing for effective detection even when using a clean non-watermarked dataset as a reference. Evaluation results demonstrate that WMD significantly outperforms naive detection methods, achieving impressive AUC scores exceeding 0.9 in most single-watermark datasets and surpassing 0.7 in more challenging multi-watermark scenarios across diverse datasets and watermarking methods. This versatile solution provides a path toward increasing accountability, transparency, and trust in digital visual content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to detect hidden marks on pictures and videos. These marks are like secret codes that can’t be easily removed or destroyed. The method uses special learning techniques to find these marks without knowing how they were created. It works by comparing the picture or video with another one that doesn’t have the mark. This helps the computer focus only on the parts of the picture or video where the mark is hiding. The new approach is very good at finding these hidden marks and can even detect multiple marks at once. This could help make sure that digital content is trustworthy and honest. |
Keywords
* Artificial intelligence * Auc
