# Efficient Provably Secure Linguistic Steganography via Range Coding

arXiv: https://arxiv.org/abs/2604.08052 · HTML: https://mlsanity.com/paper/2604.08052/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 9 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Non-Determinism of "Deterministic" LLM Settings](https://mlsanity.com/paper/2408.04667/) (2024)
- [Neural Linguistic Steganography](https://mlsanity.com/paper/1909.01496/) (2019)
- [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)
- [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)
- [Addressing Segmentation Ambiguity in Neural Linguistic Steganography](https://mlsanity.com/paper/2211.06662/) (2022)

## Built on by (most cited first, 3 of 3)

- [HiTMS: A High-Throughput Multi-Stream Linguistic Steganography Framework](https://mlsanity.com/paper/2607.23597/) (2026)
- [A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges](https://mlsanity.com/paper/2608.29077/) (2026)
- [Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion](https://mlsanity.com/paper/2609.28900/) (2026)

## Abstract (arXiv)

Linguistic steganography involves embedding secret messages within seemingly innocuous texts to enable covert communication. Provable security, which is a long-standing goal and key motivation, has been extended to language-model-based steganography. Previous provably secure approaches have achieved perfect imperceptibility, measured by zero Kullback-Leibler (KL) divergence, but at the expense of embedding capacity. In this paper, we attempt to directly use a classic entropy coding method (range coding) to achieve secure steganography, and then propose an efficient and provably secure linguistic steganographic method with a rotation mechanism. Experiments across various language models show that our method achieves around 100% entropy utilization (embedding efficiency) for embedding capacity, outperforming the existing baseline methods. Moreover, it achieves high embedding speeds (up to 1554.66 bits/s on GPT-2). The code is available at github.com/ryehr/RRC_steganography.