基于手工特征提取与结果融合的CNN音频隐写分析算法  

A CNN Based Audio Steganalysis Algorithm by Manual Feature Extraction and Result Merging

作　　者：李敬轩 胡润文 阮观奇 项世军[1] LI Jing-Xuan;HU Run-Wen;RUAN Guan-Qi;XIANG Shi-Jun(College of Information Science and Technology/College of Cyber Security,Jinan University,Guangzhou 510632)

机构地区：[1]暨南大学信息科学技术学院/网络空间安全学院,广州510632

出　　处：《计算机学报》2021年第10期2061-2075,共15页Chinese Journal of Computers

基　　金：国家自然科学基金(No.61772234);广东省科技创新战略专项资金(No.pdjh2020a0060)资助。

摘　　要：随着互联网技术的快速发展,出现了基于IP的语音传输技术,给人们带来方便的同时也带来了许多安全隐患,如不法分子利用VoIP压缩域语音传输协议传送秘密信息.因此,针对基于G.729A编码的基音隐写算法和互补邻居顶点的量化索引调制音频隐写算法,本文提出了一种基于手工特征提取与结果融合的卷积神经网络音频隐写分析算法.通过将手工提取特征与卷积神经网络相结合,可以实现在VoIP压缩域同时对基于基音的隐写算法和互补邻居顶点的量化索引调制音频隐写算法进行有效检测.实验结果表明,在同时对基音隐写算法和互补邻居顶点的量化索引调制音频隐写算法进行检测时,本文所提出的基于手工特征提取与结果融合的卷积神经网络音频隐写分析算法的检测准确率可以达到86.2%(嵌入率为100%、音频样本时长为0.1s).与现有隐写分析算法相比,在音频时长较短时,本文所提算法取得了优异的检测结果.With the rapid development of Internet technology,IP-based voice transmission technology has emerged.While bringing convenience to people,it also brings many security risks.The criminals using VoIP voice transmission protocol in compressed domains to transmit secret information has brought great challenges to social security.In this paper,for the pitch steganography algorithm and the quantized index modulation audio steganography algorithm of complementary neighbor vertex based on G.729A encoding,an audio steganalysis algorithm based on manual feature extraction and convolutional neural network is proposed.By combining manually extracted features with convolutional neural networks,it is possible to achieve effective detection of both the quantized index modulation audio steganography algorithm of complementary neighbor vertex and the pitch-based steganography algorithm in the VoIP compressed domain.Specifically,the algorithm proposed in this paper firstly extracts manual features from the G.729A speech segment(including two manual features extracted by the pitch steganography algorithm and three manual features extracted by the quantized index modulation audio steganography algorithm with complementary neighbor vertex).After using audio steganography algorithm to steganography audio samples,the five extracted manual features have been changed to vary degrees.Therefore,these five manual features can be used as one of the basis for judging whether the audio samples contain secret information.Then,after extracting the five manual features,this paper designs two different convolutional neural networks for the pitch steganography algorithm and the quantized index modulation audio steganography algorithm with complementary neighbor vertex.The two extracted manual features for the pitch steganography algorithm and the three manual features for the quantized index modulation audio steganography algorithm based on complementary neighbor vertex are input into the two different convolutional neural networks,respectively.Imm

关 键 词：隐写分析 G.729A 卷积神经网络 手工特征提取 结果融合 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]