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Spectrum Steganalysis of WAV Audio Streams

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5632))

Abstract

In this paper, we propose an audio steganalysis method called reference based Fourier Spectrum Steganalysis. The mean values and the standard deviations of the high frequency spectrum of the second and high order derivatives are extracted from the testing signals and the reference versions. A Support Vector Machine (SVM) is employed to discriminate the unadulterated carrier signals and the steganograms wherein covert messages were embedded. Experimental results show that our method delivers very good performance and holds great promise for effective detection of steganograms produced by Hide4PGP, Invisible Secrets, S-tools4 and Steghide.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, USA

    Qingzhong Liu, Andrew H. Sung & Mengyu Qiao

  2. Institute for Complex Additive Systems Analysis, New Mexico Tech, Socorro, NM 87801, USA

    Qingzhong Liu & Andrew H. Sung

Authors
  1. Qingzhong Liu
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  2. Andrew H. Sung
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  3. Mengyu Qiao
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Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Körnerstr. 10, 04107, Leipzig, Deutschland, Germany

    Petra Perner

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Liu, Q., Sung, A.H., Qiao, M. (2009). Spectrum Steganalysis of WAV Audio Streams. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_44

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  • DOI: https://doi.org/10.1007/978-3-642-03070-3_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03069-7

  • Online ISBN: 978-3-642-03070-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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