# Towards Near-imperceptible Steganographic Text

arXiv: https://arxiv.org/abs/1907.06679 · HTML: https://mlsanity.com/paper/1907.06679/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 30 of 32)

- [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)
- [On the Reliability of Watermarks for Large Language Models](https://mlsanity.com/paper/2306.04634/) (2023)
- [Unbiased Watermark for Large Language Models](https://mlsanity.com/paper/2310.10669/) (2023)
- [Perfectly Secure Steganography Using Minimum Entropy Coupling](https://mlsanity.com/paper/2210.14889/) (2022)
- [Provably Secure Generative Linguistic Steganography](https://mlsanity.com/paper/2106.02011/) (2021)
- [Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model](https://mlsanity.com/paper/2104.09833/) (2021)
- [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)
- [Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation](https://mlsanity.com/paper/2305.05010/) (2023)
- [Watermarking LLMs with Weight Quantization](https://mlsanity.com/paper/2310.11237/) (2023)
- [Generative Text Steganography with Large Language Model](https://mlsanity.com/paper/2404.10229/) (2024)
- [WAPITI: A Watermark for Finetuned Open-Source LLMs](https://mlsanity.com/paper/2410.06467/) (2024)
- [Enhancing LLM Watermark Resilience Against Both Scrubbing and Spoofing Attacks](https://mlsanity.com/paper/2507.06274/) (2025)
- [A Survey on Responsible Generative AI: What to Generate and What Not](https://mlsanity.com/paper/2404.05783/) (2024)
- [Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding](https://mlsanity.com/paper/2203.03795/) (2022)
- [Addressing Segmentation Ambiguity in Neural Linguistic Steganography](https://mlsanity.com/paper/2211.06662/) (2022)
- [OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributions](https://mlsanity.com/paper/2410.04328/) (2024)
- [Zero-shot Generative Linguistic Steganography](https://mlsanity.com/paper/2403.10856/) (2024)
- [Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models](https://mlsanity.com/paper/2508.20718/) (2025)
- [Efficient Provably Secure Linguistic Steganography via Range Coding](https://mlsanity.com/paper/2604.08052/) (2026)
- [Leveraging Generative Models for Covert Messaging: Challenges and Tradeoffs for "Dead-Drop" Deployments](https://mlsanity.com/paper/2110.07009/) (2021)
- [Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography](https://mlsanity.com/paper/2604.09066/) (2026)
- [A Content-Preserving Secure Linguistic Steganography](https://mlsanity.com/paper/2511.12565/) (2025)
- [Hide and Seek in Embedding Space: Geometry-based Steganography and Detection in Large Language Models](https://mlsanity.com/paper/2601.22818/) (2026)
- [Steganography Without Modification: Hidden Communication via LLM Seeds](https://mlsanity.com/paper/2606.09135/) (2026)
- [On Information Hiding in Natural Language Systems](https://mlsanity.com/paper/2203.06512/) (2022)
- [On Reward Structures of Markov Decision Processes](https://mlsanity.com/paper/2308.14919/) (2023)
- [Provably Secure Covert Messaging Using Image-based Diffusion Processes](https://mlsanity.com/paper/2503.10063/) (2025)
- [A Character-based Diffusion Embedding Algorithm for Enhancing the Generation Quality of Generative Linguistic Steganographic Texts](https://mlsanity.com/paper/2505.00977/) (2025)

## Abstract (arXiv)

We show that the imperceptibility of several existing linguistic steganographic systems (Fang et al., 2017; Yang et al., 2018) relies on implicit assumptions on statistical behaviors of fluent text. We formally analyze them and empirically evaluate these assumptions. Furthermore, based on these observations, we propose an encoding algorithm called patient-Huffman with improved near-imperceptible guarantees.