# TS-RNN: Text Steganalysis Based on Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1905.13087 · HTML: https://mlsanity.com/paper/1905.13087/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 20 of 20)

- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoring](https://mlsanity.com/paper/2602.23163/) (2026)
- [Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding](https://mlsanity.com/paper/2203.03795/) (2022)
- [Exploiting Language Model for Efficient Linguistic Steganalysis](https://mlsanity.com/paper/2107.12168/) (2021)
- [Zero-shot Generative Linguistic Steganography](https://mlsanity.com/paper/2403.10856/) (2024)
- [UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis](https://mlsanity.com/paper/2311.01775/) (2023)
- [Leveraging Generative Models for Covert Messaging: Challenges and Tradeoffs for "Dead-Drop" Deployments](https://mlsanity.com/paper/2110.07009/) (2021)
- [State-of-the-art Advances of Deep-learning Linguistic Steganalysis Research](https://mlsanity.com/paper/2409.01780/) (2024)
- [Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition](https://mlsanity.com/paper/2110.09814/) (2021)
- [General Framework for Reversible Data Hiding in Texts Based on Masked Language Modeling](https://mlsanity.com/paper/2206.10112/) (2022)
- [Linguistic Steganalysis via LLMs: Two Modes for Efficient Detection of Strongly Concealed Stego](https://mlsanity.com/paper/2406.04218/) (2024)
- [GTSD: Generative Text Steganography Based on Diffusion Model](https://mlsanity.com/paper/2504.19433/) (2025)
- [GSDFuse: Capturing Cognitive Inconsistencies from Multi-Dimensional Weak Signals in Social Media Steganalysis](https://mlsanity.com/paper/2505.17085/) (2025)
- [Deep Generative Models for Vehicle Speed Trajectories](https://mlsanity.com/paper/2112.08361/) (2021)
- [A Character-based Diffusion Embedding Algorithm for Enhancing the Generation Quality of Generative Linguistic Steganographic Texts](https://mlsanity.com/paper/2505.00977/) (2025)
- [Exploring AI in Steganography and Steganalysis: Trends, Clusters, and Sustainable Development Potential](https://mlsanity.com/paper/2511.12052/) (2025)
- [Text Steganography with Dynamic Codebook and Multimodal Large Language Model](https://mlsanity.com/paper/2604.20269/) (2026)
- [REED: Post-Training Representation Editing for Cross-Domain Linguistic Steganalysis](https://mlsanity.com/paper/2605.28298/) (2026)
- [FADRW: A Feature-Aware Modulated and Dynamically Reweighted Loss for Few-Shot Linguistic Steganalysis](https://mlsanity.com/paper/2606.07655/) (2026)
- [A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges](https://mlsanity.com/paper/2608.29077/) (2026)

## Abstract (arXiv)

With the rapid development of natural language processing technologies, more and more text steganographic methods based on automatic text generation technology have appeared in recent years. These models use the powerful self-learning and feature extraction ability of the neural networks to learn the feature expression of massive normal texts. Then they can automatically generate dense steganographic texts which conform to such statistical distribution based on the learned statistical patterns. In this paper, we observe that the conditional probability distribution of each word in the automatically generated steganographic texts will be distorted after embedded with hidden information. We use Recurrent Neural Networks (RNNs) to extract these feature distribution differences and then classify those features into cover text and stego text categories. Experimental results show that the proposed model can achieve high detection accuracy. Besides, the proposed model can even make use of the subtle differences of the feature distribution of texts to estimate the amount of hidden information embedded in the generated steganographic text.