# FreqMark: Frequency-Based Watermark for Sentence-Level Detection of LLM-Generated Text

arXiv: https://arxiv.org/abs/2410.10876 · HTML: https://mlsanity.com/paper/2410.10876/

## Key facts

- Cited by 7 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 20 references that are in the map.
- First posted on arXiv 9 Oct 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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [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)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [Protecting Language Generation Models via Invisible Watermarking](https://mlsanity.com/paper/2302.03162/) (2023)
- [Robust Multi-bit Natural Language Watermarking through Invariant Features](https://mlsanity.com/paper/2305.01904/) (2023)
- [Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark](https://mlsanity.com/paper/2305.10036/) (2023)
- [Deepfake Text Detection: Limitations and Opportunities](https://mlsanity.com/paper/2210.09421/) (2022)

## Built on by (most cited first, 7 of 7)

- [PMark: Towards Robust and Distortion-free Semantic-level Watermarking with Channel Constraints](https://mlsanity.com/paper/2509.21057/) (2025)
- [FDLLM: A Dedicated Detector for Black-Box LLMs Fingerprinting](https://mlsanity.com/paper/2501.16029/) (2025)
- [A Visual Semantic Adaptive Watermark grounded by Prefix-Tuning for Large Vision-Language Model](https://mlsanity.com/paper/2601.07291/) (2026)
- [LLM-Text Watermarking based on Lagrange Interpolation](https://mlsanity.com/paper/2505.05712/) (2025)
- [Stylometry recognizes human and LLM-generated texts in short samples](https://mlsanity.com/paper/2507.00838/) (2025)
- [Synchronization-Free Algebraic Fingerprints for Large Language Models: From Autoregressive to Diffusion Models](https://mlsanity.com/paper/2607.16648/) (2026)
- [Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text](https://mlsanity.com/paper/2608.18102/) (2026)

## Abstract (arXiv)

The increasing use of Large Language Models (LLMs) for generating highly coherent and contextually relevant text introduces new risks, including misuse for unethical purposes such as disinformation or academic dishonesty. To address these challenges, we propose FreqMark, a novel watermarking technique that embeds detectable frequency-based watermarks in LLM-generated text during the token sampling process. The method leverages periodic signals to guide token selection, creating a watermark that can be detected with Short-Time Fourier Transform (STFT) analysis. This approach enables accurate identification of LLM-generated content, even in mixed-text scenarios with both human-authored and LLM-generated segments. Our experiments demonstrate the robustness and precision of FreqMark, showing strong detection capabilities against various attack scenarios such as paraphrasing and token substitution. Results show that FreqMark achieves an AUC improvement of up to 0.98, significantly outperforming existing detection methods.