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Mobile Information Systems
Volume 2017, Article ID 5418978, 18 pages
Research Article

Detecting Steganography of Adaptive Multirate Speech with Unknown Embedding Rate

1College of Computer Science and Technology, National Huaqiao University, Xiamen 361021, China
2Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Correspondence should be addressed to Hui Tian; nc.ude.uqh@naith and Tian Wang; nc.ude.uqh@naitgnaw

Received 9 December 2016; Accepted 23 April 2017; Published 18 May 2017

Academic Editor: Elio Masciari

Copyright © 2017 Hui Tian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Steganalysis of adaptive multirate (AMR) speech is a significant research topic for preventing cybercrimes based on steganography in mobile speech services. Differing from the state-of-the-art works, this paper focuses on steganalysis of AMR speech with unknown embedding rate, where we present three schemes based on support-vector-machine to address the concern. The first two schemes evolve from the existing image steganalysis schemes, which adopt different global classifiers. One is trained on a comprehensive speech sample set including original samples and steganographic samples with various embedding rates, while the other is trained on a particular speech sample set containing original samples and steganographic samples with uniform distributions of embedded information. Further, we present a hybrid steganalysis scheme, which employs Dempster–Shafer theory (DST) to fuse all the evidence from multiple specific classifiers and provide a synthesized detection result. All the steganalysis schemes are evaluated using the well-selected feature set based on statistical characteristics of pulse pairs and compared with the optimal steganalysis that adopts specialized classifiers for corresponding embedding rates. The experimental results demonstrate that all the three steganalysis schemes are feasible and effective for detecting the existing steganographic methods with unknown embedding rates in AMR speech streams, while the DST-based scheme outperforms the others overall.