# An Entropy-based Text Watermarking Detection Method

arXiv: https://arxiv.org/abs/2403.13485 · HTML: https://mlsanity.com/paper/2403.13485/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 15 of 15)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [Robust Distortion-free Watermarks for Language Models](https://mlsanity.com/paper/2307.15593/) (2023)
- [Provable Robust Watermarking for AI-Generated Text](https://mlsanity.com/paper/2306.17439/) (2023)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Who Wrote this Code? Watermarking for Code Generation](https://mlsanity.com/paper/2305.15060/) (2023)
- [A Survey of Text Watermarking in the Era of Large Language Models](https://mlsanity.com/paper/2312.07913/) (2023)
- [Robust Multi-bit Natural Language Watermarking through Invariant Features](https://mlsanity.com/paper/2305.01904/) (2023)
- [A Robust Semantics-based Watermark for Large Language Model against Paraphrasing](https://mlsanity.com/paper/2311.08721/) (2023)
- [Watermarking Text Generated by Black-Box Language Models](https://mlsanity.com/paper/2305.08883/) (2023)
- [Publicly-Detectable Watermarking for Language Models](https://mlsanity.com/paper/2310.18491/) (2023)
- [Text-to-Table: A New Way of Information Extraction](https://mlsanity.com/paper/2109.02707/) (2021)

## Built on by (most cited first, 30 of 75)

- [MarkLLM: An Open-Source Toolkit for LLM Watermarking](https://mlsanity.com/paper/2405.10051/) (2024)
- [Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2502.06872/) (2025)
- [Revisiting the Robustness of Watermarking to Paraphrasing Attacks](https://mlsanity.com/paper/2411.05277/) (2024)
- [MorphMark: Flexible Adaptive Watermarking for Large Language Models](https://mlsanity.com/paper/2505.11541/) (2025)
- [Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?](https://mlsanity.com/paper/2502.11598/) (2025)
- [Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking](https://mlsanity.com/paper/2503.04636/) (2025)
- [CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality](https://mlsanity.com/paper/2504.17309/) (2025)
- [Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks](https://mlsanity.com/paper/2505.05190/) (2025)
- [PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints](https://mlsanity.com/paper/2509.21057/) (2025)
- [Can Watermarked LLMs be Identified by Users via Crafted Prompts?](https://mlsanity.com/paper/2410.03168/) (2024)
- [WAPITI: A Watermark for Finetuned Open-Source LLMs](https://mlsanity.com/paper/2410.06467/) (2024)
- [VLA-Mark: A cross modal watermark for large vision-language alignment model](https://mlsanity.com/paper/2507.14067/) (2025)
- [Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code](https://mlsanity.com/paper/2502.18851/) (2025)
- [WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents](https://mlsanity.com/paper/2409.05112/) (2024)
- [Watermark under Fire: A Robustness Evaluation of LLM Watermarking](https://mlsanity.com/paper/2411.13425/) (2024)
- [Enhancing LLM Watermark Resilience Against Both Scrubbing and Spoofing Attacks](https://mlsanity.com/paper/2507.06274/) (2025)
- [Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack](https://mlsanity.com/paper/2506.01011/) (2025)
- [SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders](https://mlsanity.com/paper/2508.08211/) (2025)
- [Discovering Spoofing Attempts on Language Model Watermarks](https://mlsanity.com/paper/2410.02693/) (2024)
- [Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking](https://mlsanity.com/paper/2505.14112/) (2025)
- [DITTO: A Spoofing Attack Framework on Watermarked LLMs via Knowledge Distillation](https://mlsanity.com/paper/2510.10987/) (2025)
- [From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework for Large Language Models](https://mlsanity.com/paper/2505.09924/) (2025)
- [Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm](https://mlsanity.com/paper/2509.07287/) (2025)
- [On Evaluating The Performance of Watermarked Machine-Generated Texts Under Adversarial Attacks](https://mlsanity.com/paper/2407.04794/) (2024)
- [Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations](https://mlsanity.com/paper/2502.16699/) (2025)
- [Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models](https://mlsanity.com/paper/2508.20718/) (2025)
- [PathMark: Protecting Intellectual Property of Mixture-of-Expert LLMs via Path Watermarks](https://mlsanity.com/paper/2607.03688/) (2026)
- [A Certified Robust Watermark For Large Language Models](https://mlsanity.com/paper/2409.19708/) (2024)
- [BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks](https://mlsanity.com/paper/2501.12174/) (2025)
- [Watermarking Degrades Alignment in Language Models: Analysis and Mitigation](https://mlsanity.com/paper/2506.04462/) (2025)

## Abstract (arXiv)

Text watermarking algorithms for large language models (LLMs) can effectively identify machine-generated texts by embedding and detecting hidden features in the text. Although the current text watermarking algorithms perform well in most high-entropy scenarios, its performance in low-entropy scenarios still needs to be improved. In this work, we opine that the influence of token entropy should be fully considered in the watermark detection process, i.e., the weight of each token during watermark detection should be customized according to its entropy, rather than setting the weights of all tokens to the same value as in previous methods. Specifically, we propose Entropy-based Text Watermarking Detection (EWD) that gives higher-entropy tokens higher influence weights during watermark detection, so as to better reflect the degree of watermarking. Furthermore, the proposed detection process is training-free and fully automated. From the experiments, we demonstrate that our EWD can achieve better detection performance in low-entropy scenarios, and our method is also general and can be applied to texts with different entropy distributions. Our code and data is availablefootnoteurlhttps://github.com/luyijian3/EWD. Additionally, our algorithm could be accessed through MarkLLM citepan2024markllmfootnoteurlhttps://github.com/THU-BPM/MarkLLM.