Text Steganalysis with Attentional LSTM-CNN

TL;DR

This paper introduces an attentional LSTM-CNN model that effectively detects steganography in texts by capturing semantic, local, and long-distance contextual features, achieving state-of-the-art results.

Contribution

The paper presents a novel neural network architecture combining attention, LSTM, and CNN for improved text steganalysis performance.

Findings

Achieves state-of-the-art accuracy in text steganalysis

Effectively captures semantic and contextual features

Outperforms existing methods in detection tasks

Abstract

With the rapid development of Natural Language Processing (NLP) technologies, text steganography methods have been significantly innovated recently, which poses a great threat to cybersecurity. In this paper, we propose a novel attentional LSTM-CNN model to tackle the text steganalysis problem. The proposed method firstly maps words into semantic space for better exploitation of the semantic feature in texts and then utilizes a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) recurrent neural networks to capture both local and long-distance contextual information in steganography texts. In addition, we apply attention mechanism to recognize and attend to important clues within suspicious sentences. After merge feature clues from Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), we use a softmax layer to categorize the input text as cover or stego. Experiments showed that our model can achieve the state-of-art result in the text steganalysis task.