Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals
Steganalysis of the quantization index modulation (QIM) steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization (VQ) codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network (QCCN) based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine (SVM) classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.