# A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges

arXiv: https://arxiv.org/abs/2608.29077 · HTML: https://mlsanity.com/paper/2608.29077/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 41 of its 230 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 30 of 41)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [Non-Determinism of "Deterministic" LLM Settings](https://mlsanity.com/paper/2408.04667/) (2024)
- [Neural Linguistic Steganography](https://mlsanity.com/paper/1909.01496/) (2019)
- [Generating Steganographic Text with LSTMs](https://mlsanity.com/paper/1705.10742/) (2017)
- [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)
- [Towards Near-imperceptible Steganographic Text](https://mlsanity.com/paper/1907.06679/) (2019)
- [Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model](https://mlsanity.com/paper/2104.09833/) (2021)
- [The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence](https://mlsanity.com/paper/2403.13784/) (2024)
- [TS-RNN: Text Steganalysis Based on Recurrent Neural Networks](https://mlsanity.com/paper/1905.13087/) (2019)
- [Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding](https://mlsanity.com/paper/2010.00677/) (2020)
- [SparSamp: Efficient Provably Secure Steganography Based on Sparse Sampling](https://mlsanity.com/paper/2503.19499/) (2025)
- [Automatically Generate Steganographic Text Based on Markov Model and Huffman Coding](https://mlsanity.com/paper/1811.04720/) (2018)
- [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)
- [Exploiting Language Model for Efficient Linguistic Steganalysis](https://mlsanity.com/paper/2107.12168/) (2021)
- [Autoregressive Linguistic Steganography Based on BERT and Consistency Coding](https://mlsanity.com/paper/2203.13972/) (2022)
- [Provably Robust and Secure Steganography in Asymmetric Resource Scenario](https://mlsanity.com/paper/2407.13499/) (2024)
- [Towards Next-Generation Steganalysis: LLMs Unleash the Power of Detecting Steganography](https://mlsanity.com/paper/2405.09090/) (2024)
- [Dynamically Allocated Interval-Based Generative Linguistic Steganography with Roulette Wheel](https://mlsanity.com/paper/2401.15656/) (2024)
- [Efficient Provably Secure Linguistic Steganography via Range Coding](https://mlsanity.com/paper/2604.08052/) (2026)
- [Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph](https://mlsanity.com/paper/2006.08339/) (2020)
- [Pseudo-label Based Domain Adaptation for Zero-Shot Text Steganalysis](https://mlsanity.com/paper/2406.18565/) (2024)

## Abstract (arXiv)

Linguistic steganography hides secret messages in natural language text. Large language models (LLMs) have reshaped the field, but a systematic account of how these scattered advances collectively reshape the field in this new era is still missing. We provide one along four axes: 148 steganographic methods, 60 linguistic steganalysis countermeasures, 23 evaluation metrics, and 9 open challenges, each with taxonomies, reviews, and adoption analyses. Cutting across these axes, we identify five specific paradigm shifts in the LLM era: (1) from covertext modification to prompt-only generation, (2) from heuristic to provable security, (3) from white-box symmetric LMs to black-box or asymmetric access, (4) from security-centric designs to joint optimization, and (5) from text-quality concerns to engineering issues. The survey aims to serve as both a reference and a roadmap for practical and responsible linguistic steganography in the LLM era.