Steganalysis of AMR Speech Stream Based on Multi-Domain Information Fusion

Traditional machine learning-based steganalysis methods on compressed speech in VoIP applications have achieved great success. However, in these methods, there is a dilemma between the effectiveness of modeling the steganographic carrier and the high dimensionality of extracted features. Especially for small-sized and low embedding rate samples, most existing methods do not perform well enough. To deal with this issue, we present MDoIF- an Adaptive Multi-Rate (AMR) steganalysis of compressed speech based on multi-domain information fusion. In order to fully extract the information reflecting the change of carrier correlation before and after VoIP steganography, we construct a Bayesian network with FCB parameters in compressed speech as the vertices, and quantify link strength between codebook parameters. On this basis, we design a multi-domain feature extraction algorithm, supplemented by an information-theoretic measure-based feature selection algorithm for dimensionality reduction, which can significantly improve the performance of MDoIF. To evaluate the performance of our method, we conduct comprehensive experiments on MDoIF and existing models. Experimental results show that MDoIF performs effectively on various AMR steganalysis tasks with excellent detection accuracy. Particularly for small-sized and low embedding rate samples, MDoIF surpasses the state-of-the-art methods.