Real-Time Text Steganalysis Based on Multi-Stage Transfer Learning

With the extensive use of texts on social network, text steganography, which protects several sensitive messages by embedding secret data into normal texts, has attracted widespread attention. As an adversary, text steganalysis which reveals the existence of hidden messages is also important. Recently, Deep Neural Networks (DNNs) have led to significant improvements in text steganalysis. However, the deeper and wider DNNs cause the increase of inference time, which restricts the practicality of text steganalysis. In this paper, we propose an effective and real-time text steganalysis method based on multi-stage transfer learning to enhance inference efficiency and detection performance simultaneously. The experimental results show that the proposed text steganalysis method can outperform previously reported methods in terms of detection accuracy and inference efficiency.

Automated summary

This paper proposes an effective and real-time text steganalysis method based on multi-stage transfer learning to enhance inference efficiency and detection performance simultaneously. The experimental results show that the proposed method outperforms previously reported methods in terms of detection accuracy and inference efficiency.