# DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

arXiv: https://arxiv.org/abs/2305.05773 · HTML: https://mlsanity.com/paper/2305.05773/

## Key facts

- Cited by 23 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 7 of its 24 references that are in the map.
- First posted on arXiv 9 May 2023.

## 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, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)

## Built on by (most cited first, 23 of 23)

- [Publicly-Detectable Watermarking for Language Models](https://mlsanity.com/paper/2310.18491/) (2023)
- [Provably Robust Multi-bit Watermarking for AI-generated Text](https://mlsanity.com/paper/2401.16820/) (2024)
- [PostMark: A Robust Blackbox Watermark for Large Language Models](https://mlsanity.com/paper/2406.14517/) (2024)
- [Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach](https://mlsanity.com/paper/2410.02890/) (2024)
- [CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality](https://mlsanity.com/paper/2504.17309/) (2025)
- [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://mlsanity.com/paper/2502.05240/) (2025)
- [Robust Multi-bit Text Watermark with LLM-based Paraphrasers](https://mlsanity.com/paper/2412.03123/) (2024)
- [Watermarking Language Models through Language Models](https://mlsanity.com/paper/2411.05091/) (2024)
- [PersonaMark: Personalized LLM watermarking for model protection and user attribution](https://mlsanity.com/paper/2409.09739/) (2024)
- [RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2501.05249/) (2025)
- [Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking](https://mlsanity.com/paper/2505.14112/) (2025)
- [WaterMod: Modular Token-Rank Partitioning for Probability-Balanced LLM Watermarking](https://mlsanity.com/paper/2511.07863/) (2025)
- [On Evaluating The Performance of Watermarked Machine-Generated Texts Under Adversarial Attacks](https://mlsanity.com/paper/2407.04794/) (2024)
- [CredID: Credible Multi-Bit Watermark for Large Language Models Identification](https://mlsanity.com/paper/2412.03107/) (2024)
- [Majority Bit-Aware Watermarking For Large Language Models](https://mlsanity.com/paper/2508.03829/) (2025)
- [EditMark: Watermarking Large Language Models based on Model Editing](https://mlsanity.com/paper/2510.16367/) (2025)
- [XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts](https://mlsanity.com/paper/2604.05242/) (2026)
- [Learning to Watermark: A Selective Watermarking Framework for Large Language Models via Multi-Objective Optimization](https://mlsanity.com/paper/2510.15976/) (2025)
- [Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation](https://mlsanity.com/paper/2607.05354/) (2026)
- [PRIVMARK: Private Large Language Models Watermarking with MPC](https://mlsanity.com/paper/2509.24624/) (2025)
- [Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints](https://mlsanity.com/paper/2604.08759/) (2026)
- [Can Watermarking Techniques Help Prevent LLM Model Stealing?](https://mlsanity.com/paper/2607.10794/) (2026)
- [Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text](https://mlsanity.com/paper/2608.18102/) (2026)

## Abstract (arXiv)

The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities of text generators. With the potential for misuse escalating, the importance of discerning whether texts are human-authored or generated by LLMs has become paramount. Several preceding studies have ventured to address this challenge by employing binary classifiers to differentiate between human-written and LLM-generated text. Nevertheless, the reliability of these classifiers has been subject to question. Given that consequential decisions may hinge on the outcome of such classification, it is imperative that text source detection is of high caliber. In light of this, the present paper introduces DeepTextMark, a deep learning-driven text watermarking methodology devised for text source identification. By leveraging Word2Vec and Sentence Encoding for watermark insertion, alongside a transformer-based classifier for watermark detection, DeepTextMark epitomizes a blend of blindness, robustness, imperceptibility, and reliability. As elaborated within the paper, these attributes are crucial for universal text source detection, with a particular emphasis in this paper on text produced by LLMs. DeepTextMark offers a viable "add-on" solution to prevailing text generation frameworks, requiring no direct access or alterations to the underlying text generation mechanism. Experimental evaluations underscore the high imperceptibility, elevated detection accuracy, augmented robustness, reliability, and swift execution of DeepTextMark.