F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network

Chuanpeng Guo,Liusheng Huang,Wei Yang,Mengxia Shuai

doi:10.1155/2021/1627486

Abstract

Traditional machine learning-based steganalysis methods on compressed speech have achieved great success in the field of communication security. However, previous studies lacked mathematical modeling of the correlation between codewords, and there is still room for improvement in steganalysis for small-sized and low embedding rate samples. To deal with the challenge, we use Bayesian networks to measure different types of correlations between codewords in linear prediction code and present F3SNet—a four-step strategy: embedding, encoding, attention, and classification for quantization index modulation steganalysis of compressed speech based on the hierarchical attention network. Among them, embedding converts codewords into high-density numerical vectors, encoding uses the memory characteristics of LSTM to retain more information by distributing it among all its vectors, and attention further determines which vectors have a greater impact on the final classification result. To evaluate the performance of F3SNet, we make a comprehensive comparison of F3SNet with existing steganography methods. Experimental results show that F3SNet surpasses the state-of-the-art methods, particularly for small-sized and low embedding rate samples.

Highlights

As an effective way to secretly transfer information over the Internet, steganography uses the redundancy of digital carriers to accomplish secret information embedding
(2) We present F3SNet, a four-step strategy for quantization index modulation (QIM) steganalysis method based on the hierarchical attention network. rough a four-step strategy, we encode the numerical codeword vectors into multiple memory vectors, select a set of vectors that have the greatest impact on the classification result to prevent information overload, and achieve efficient steganography classification, even in special cases, such as small size and low embedding rate
Steganalysis based on deep learning can automatically extract the intrinsic features of the carrier, avoiding the complexity of establishing the model. erefore, we propose a steganalysis method that utilizes the advantages of RNN and attention mechanism

Summary

Introduction

As an effective way to secretly transfer information over the Internet, steganography uses the redundancy of digital carriers to accomplish secret information embedding. Some methods which embed secret messages into the bitstream during the encoding process have been proposed, such as quantization index modulation (QIM) steganography [4,5,6], fixed codebook (FCB) steganography [7,8,9], and pitch modulation (PM) steganography [10, 11]. Especially support vector machine (SVM), have been widely used in the field of steganalysis of both traditional media and VoIP streams. As the basis of low-rate speech coding, the basic idea of linear predictive analysis (LPA) is to use the correlation of the speech signal to approximate the sample value at the current moment with the linear combination of several past speech samples. Linear predictive coding is mainly divided into three processes: LPA, line spectrum pair (LSP) analysis, and vector quantization (VQ). To further balance the bit rate and quantization accuracy, vector quantization tveecchtonro→lCogkythisatuissedclotosessetatrochthtehveeccotdoreb→pootko for be the codeword quantized in a certain distance, and the sequence number k of the codeword vector is obtained as the quantization result

Methods

Results

Conclusion