Steganalysis of Low Embedding Rate CNV-QIM in Speech

作     者：Wanxia Yang Miaoqi Li Beibei Zhou Yan Liu Kenan Liu Zhiyu Hu 

作者机构：Mechanical and Electrical Engineering CollegeGansu Agricultural UniversityLanzhou730070China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2021年第128卷第8期

页      面：623-637页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This research was supported by the National Natural Science Foundation of China(No.61862002) 

主　　题：CNV-QIM steganography BiLSTM steganalysis VoIP speech 

摘      要：To address the difficulty of detecting low embedding rate and high-concealment CNV-QIM(complementary neighbor vertices-quantization index modulation)steganography in low bit-rate speech codec,the code-word correlation model based on a BiLSTM(bi-directional long short-term memory)neural network is built to obtain the correlation features of the LPC codewords in speech codec in this ***,softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP *** experimental results show that for speech steganography of short samples with low embedding rate,the BiLSTM method in this paper has a superior detection accuracy than state-of-the-art methods of the RNN-SM(recurrent neural network-steganalysis model)and SS-QCCN(simplest strong quantization codeword correlation network).At an embedding rate of 20%and a duration of 3 s,the detection accuracy of BiLSTM method reaches 75.7%,which is higher than that of RNNSM by 11.7%.Furthermore,the average testing time of samples(100%embedding)is 0.3 s,which shows that the method can realize real-time steganography detection of VoIP streams.