Steganalysis of Low Embedding Rate CNV-QIM in Speech

Abstract

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 paper. Then, softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP streams. The 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 RNN-SM 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.