# Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model

arXiv: https://arxiv.org/abs/2104.09833 · HTML: https://mlsanity.com/paper/2104.09833/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding](https://mlsanity.com/paper/2009.03015/) (2020)
- [Neural Linguistic Steganography](https://mlsanity.com/paper/1909.01496/) (2019)
- [Generating Steganographic Text with LSTMs](https://mlsanity.com/paper/1705.10742/) (2017)
- [Towards Near-imperceptible Steganographic Text](https://mlsanity.com/paper/1907.06679/) (2019)
- [Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding](https://mlsanity.com/paper/2010.00677/) (2020)

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

- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (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)
- [RADAR: Robust AI-Text Detection via Adversarial Learning](https://mlsanity.com/paper/2307.03838/) (2023)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [Unbiased Watermark for Large Language Models](https://mlsanity.com/paper/2310.10669/) (2023)
- [Robust Multi-bit Natural Language Watermarking through Invariant Features](https://mlsanity.com/paper/2305.01904/) (2023)
- [DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning](https://mlsanity.com/paper/2410.20964/) (2024)
- [LLMDet: A Third Party Large Language Models Generated Text Detection Tool](https://mlsanity.com/paper/2305.15004/) (2023)
- [Embarrassingly Simple Text Watermarks](https://mlsanity.com/paper/2310.08920/) (2023)
- [Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey](https://mlsanity.com/paper/2310.15264/) (2023)
- [WatME: Towards Lossless Watermarking Through Lexical Redundancy](https://mlsanity.com/paper/2311.09832/) (2023)
- [Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs](https://mlsanity.com/paper/2407.04411/) (2024)
- [Downstream Trade-offs of a Family of Text Watermarks](https://mlsanity.com/paper/2311.09816/) (2023)
- [WAPITI: A Watermark for Finetuned Open-Source LLMs](https://mlsanity.com/paper/2410.06467/) (2024)
- [A Survey on Responsible Generative AI: What to Generate and What Not](https://mlsanity.com/paper/2404.05783/) (2024)
- [Addressing Segmentation Ambiguity in Neural Linguistic Steganography](https://mlsanity.com/paper/2211.06662/) (2022)
- [Autoregressive Linguistic Steganography Based on BERT and Consistency Coding](https://mlsanity.com/paper/2203.13972/) (2022)
- [Provably Secure Disambiguating Neural Linguistic Steganography](https://mlsanity.com/paper/2403.17524/) (2024)
- [TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection](https://mlsanity.com/paper/2605.12456/) (2026)
- [How Good is Post-Hoc Watermarking With Language Model Rephrasing?](https://mlsanity.com/paper/2512.16904/) (2025)
- [SLAM: Structural Linguistic Activation Marking for Language Models](https://mlsanity.com/paper/2605.05443/) (2026)
- [CoreMark: Toward Robust and Universal Text Watermarking Technique](https://mlsanity.com/paper/2506.23066/) (2025)
- [HiTMS: A High-Throughput Multi-Stream Linguistic Steganography Framework](https://mlsanity.com/paper/2607.23597/) (2026)
- [On Information Hiding in Natural Language Systems](https://mlsanity.com/paper/2203.06512/) (2022)
- [I Know You Did Not Write That! A Sampling Based Watermarking Method for Identifying Machine Generated Text](https://mlsanity.com/paper/2311.18054/) (2023)
- [Text Steganography with Dynamic Codebook and Multimodal Large Language Model](https://mlsanity.com/paper/2604.20269/) (2026)
- [On Seeding Watermarks to Detect Verbatim LLM Copy-Paste Responses](https://mlsanity.com/paper/2605.16336/) (2026)
- [A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges](https://mlsanity.com/paper/2608.29077/) (2026)
- [TripPattern: A Pattern-based Text Watermarking Method for Large Language Models](https://mlsanity.com/paper/2609.12472/) (2026)

## Abstract (arXiv)

With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generation-based ones. While the latter's payload capacity is impressive, generating genuine-looking texts remains challenging. In this paper, we revisit edit-based linguistic steganography, with the idea that a masked language model offers an off-the-shelf solution. The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. It is also shown to be more secure against automatic detection than a generation-based method while offering better control of the security/payload capacity trade-off.