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Multimedia Tools and Applications

, Volume 55, Issue 3, pp 525–556 | Cite as

Audio steganalysis of spread spectrum information hiding based on statistical moment and distance metric

  • Wei ZengEmail author
  • Ruimin Hu
  • Haojun Ai
Article

Abstract

Audio information hiding has attracted more attentions recently. Spread spectrum (SS) technique has developed rapidly in this area due to the advantages of good robustness and immunity to noise attack. Accordingly detecting the SS hiding effectively and verifying the presence of the secrete message are important issues. In this paper we present two steganalysis algorithms for SS hiding. Both the two methods are based on machine learning theory and discrete wavelet transform (DWT). In the algorithm I, we introduce Gaussian mixture model (GMM) and generalize Gaussian distribution (GGD) to character the probability distribution of wavelet sub-band. Then the absolute probability distribution function (PDF) moment is extracted as feature vectors. In the algorithm II, we propose distance metric between GMM and GGD of wavelet sub-band to distinguish cover and stego audio. Four distance metrics (Kullback-Leibler Distance, Bhattacharyya Distance, Earth Mover’s Distance, L2 Distance) are calculated as feature vectors. The support vector machine (SVM) classifier is utilized for classification. The experiment results of both two proposed algorithms can achieve better detecting performance. Even when embedding strength gets 0.0005, the correct detection rate can reach up to 90%. Its simplicity and extensibility indicate further application in other audio steganalysis.

Keywords

Steganography Steganalysis Audio Spread spectrum 

Notes

Acknowledgments

This work reported is supported by the National Science Foundation of China (grant 60832002), Important National Science & Technology Specific Projects (grant 2010ZX03004-003) and self-research program of Wuhan University (grant 6081012). We thank the anonymous reviewers for their insightful comments that help improve the presentation.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.National Engineering Research Center for Multimedia SoftwareWuhan UniversityWuhanChina

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