Wanxia Yang*, Miaoqi Li, Beibei Zhou, Yan Liu, Kenan Liu, Zhiyu Hu
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 623-637, 2021, DOI:10.32604/cmes.2021.015629
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… More >
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