Abstract
Analysis-by-synthesis linear predictive coding (AbS-LPC) is widely used in a variety of low-bit-rate speech codecs. The existing steganalysis methods for AbS-LPC low-bit-rate compressed speech steganography are specifically designed for one certain category of steganography methods, thus lacking generalization capability. In this paper, a common method for detecting multiple steganographies in low-bit-rate compressed speech based on a code element Bayesian network is proposed. In an AbS-LPC low-bit-rate compressed speech stream, spatiotemporal correlations exist between the code elements, and steganography will eventually change the values of these code elements. Thus, the method presented in this paper is developed from the code element perspective. It consists of constructing a code element Bayesian network based on the strong correlations between code elements, learning the network parameters by utilizing a Dirichlet distribution as the prior distribution, and finally implementing steganalysis based on Bayesian inference. Experimental results demonstrate that the proposed method performs better than the existing steganalysis methods for detecting multiple steganographies in the AbS-LPC low-bit-rate compressed speech.
Highlights
Information hiding, called steganography, is an ancient but effective technique of embedding secret information into innocent carriers without perception
For the detection of steganography methods in the third category, Tian et al [45] present a distributed steganalysis scheme based on four types of features: histograms, differential histograms, Markov transition matrices and differential Markov transition matrices
We propose a common method for analysis-by-synthesis linear predictive coding (AbS-LPC) low-bit-rate compressed speech steganography from the perspective of code elements (CEs)
Summary
Information hiding, called steganography, is an ancient but effective technique of embedding secret information into innocent carriers without perception. Most methods of speech steganography utilize AbS-LPC low-bit-rate speech codecs to embed secret information for covert communication. Some existing general steganalysis methods are based on uncompressed domain features, such as mel-frequency cepstral coefficient (MFCC) features [47]–[49] These methods can be used for the general steganalysis of AbS-LPC low-bit-rate compressed speech, they are designed for the detection of steganography in uncompressed speech files. A common method for detecting multiple steganographies in AbS-LPC low-bit-rate compressed speech is needed.
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