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

## TL;DR

This paper introduces TS-RNN, a neural network-based method for detecting text steganography by analyzing distribution differences in generated texts, achieving high accuracy and estimating hidden information quantity.

## Contribution

The paper presents a novel RNN-based approach for text steganalysis that effectively detects hidden information and estimates its amount in generated texts.

## Key findings

- High detection accuracy achieved
- Effective estimation of hidden information quantity
- Utilizes subtle distribution differences in texts

## Abstract

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.