mlsanityData to 7 Oct 2026

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

arXiv 2104.09833 ↗First posted 20 Apr 2021cs.CLcited by 28

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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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

14 of 26 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020Longformer: The Long-Document Transformercited by 3,362
  5. 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
  6. 2015Teaching Machines to Read and Comprehendcited by 1,039
  7. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  8. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
  9. 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 260
  10. 2020Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hidingcited by 121
  11. 2019Neural Linguistic Steganographycited by 73
  12. 2017Generating Steganographic Text with LSTMscited by 41
2 more
  1. 2019Towards Near-imperceptible Steganographic Textcited by 30
  2. 2020Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Codingcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 12; 2024: 4; 2025: 2; 2026: 7.

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  1. 2023A Watermark for Large Language Modelscited by 667
  2. 2023Robust Distortion-free Watermarks for Language Modelscited by 279
  3. 2023Provable Robust Watermarking for AI-Generated Textcited by 259
  4. 2023RADAR: Robust AI-Text Detection via Adversarial Learningcited by 187
  5. 2023The Science of Detecting LLM-Generated Textscited by 126
  6. 2023Robust Multi-bit Natural Language Watermarking through Invariant Featurescited by 76
  7. 2024DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learningcited by 52
  8. 2023LLMDet: A Third Party Large Language Models Generated Text Detection Toolcited by 44
  9. 2023Towards Possibilities & Impossibilities of AI-generated Text Detection: A Surveycited by 28
  10. 2023Embarrassingly Simple Text Watermarkscited by 23
  11. 2023WatME: Towards Lossless Watermarking Through Lexical Redundancycited by 22
  12. 2023Downstream Trade-offs of a Family of Text Watermarkscited by 14
16 more
  1. 2024Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMscited by 14
  2. 2024A Survey on Responsible Generative AI: What to Generate and What Notcited by 8
  3. 2022Addressing Segmentation Ambiguity in Neural Linguistic Steganographycited by 7
  4. 2022Autoregressive Linguistic Steganography Based on BERT and Consistency Codingcited by 6
  5. 2024Provably Secure Disambiguating Neural Linguistic Steganographycited by 5
  6. 2025How Good is Post-Hoc Watermarking With Language Model Rephrasing?cited by 2
  7. 2026TextSeal: A Localized LLM Watermark for Provenance & Distillation Protectioncited by 2
  8. 2025CoreMark: Toward Robust and Universal Text Watermarking Techniquecited by 1
  9. 2026SLAM: Structural Linguistic Activation Marking for Language Modelscited by 1
  10. 2026HiTMS: A High-Throughput Multi-Stream Linguistic Steganography Frameworkcited by 1
  11. 2022On Information Hiding in Natural Language Systemscited by 0
  12. 2023I Know You Did Not Write That! A Sampling Based Watermarking Method for Identifying Machine Generated Textcited by 0
  13. 2026Text Steganography with Dynamic Codebook and Multimodal Large Language Modelcited by 0
  14. 2026On Seeding Watermarks to Detect Verbatim LLM Copy-Paste Responsescited by 0
  15. 2026A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challengescited by 0
  16. 2026TripPattern: A Pattern-based Text Watermarking Method for Large Language Modelscited by 0