Summary of Robust Multi-bit Text Watermark with Llm-based Paraphrasers, by Xiaojun Xu et al.
Robust Multi-bit Text Watermark with LLM-based Paraphrasers
by Xiaojun Xu, Jinghan Jia, Yuanshun Yao, Yang Liu, Hang Li
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 imperceptible multi-bit text watermarking approach is proposed, leveraging large language models (LLMs) for paraphrasing. The method fine-tunes a pair of LLM paraphrasers to produce differing outputs that can be decoded to reveal the embedded binary code. This is achieved by alternating between two paraphrasers at the sentence level and utilizing a text classifier as the decoder. Experimental results demonstrate high detection accuracy (over 99.99%) with small text paraphrasers, while preserving original semantic information. Additionally, the pipeline exhibits robustness against word substitution and sentence paraphrasing perturbations, generalizing well to out-of-distributional data. The stealthiness of the watermark is evaluated using LLM-based metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a special kind of invisible stamp that can be added to text messages without changing what they say. This “watermark” is created by using special computer programs called language models to rephrase sentences in slightly different ways. By comparing these different versions, it’s possible to detect the presence of this watermark and even decode its contents. The researchers developed a way to embed this watermark into text while keeping the original meaning intact, and tested it on large datasets to see how well it worked. They found that their method was very effective at detecting the watermark, even when the text had been changed in small ways. This could be useful for verifying the authenticity of digital documents or messages. |
Keywords
» Artificial intelligence » Decoder
