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Multi-class Blind Steganalysis Based on Image Run-Length Analysis

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Digital Watermarking (IWDW 2009)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 5703))

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Abstract

In this paper, we investigate our previously developed run-length based features for multi-class blind image steganalysis. We construct a Support Vector Machine classifier for multi-class recognition for both spatial and frequency domain based steganographic algorithms. We also study hierarchical and non-hierarchical multi-class schemes and compare their performance for steganalysis. Experimental results demonstrate that our approach is able to classify different stego images according to their embedding techniques based on appropriate supervised learning. It is also shown that the hierarchical scheme performs better in our experiments.

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Author information

Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P.O. Box 2728, Beijing

    Jing Dong, Wei Wang & Tieniu Tan

Authors
  1. Jing Dong
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  2. Wei Wang
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  3. Tieniu Tan
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Physical Sciences, Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Anthony T. S. Ho

  2. New Jersey Institute of Technology, University Heights, 07102-1982, Newark, NJ, USA

    Yun Q. Shi

  3. Multimedia Security Lab, Korea University, Science Campus, 136-701, Seoul, Korea

    H. J. Kim

  4. Department of Information Engineering, University of Siena, Via Roma 56, 53100, Siena, Italy

    Mauro Barni

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© 2009 Springer-Verlag Berlin Heidelberg

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Dong, J., Wang, W., Tan, T. (2009). Multi-class Blind Steganalysis Based on Image Run-Length Analysis. In: Ho, A.T.S., Shi, Y.Q., Kim, H.J., Barni, M. (eds) Digital Watermarking. IWDW 2009. Lecture Notes in Computer Science, vol 5703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03688-0_19

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  • DOI: https://doi.org/10.1007/978-3-642-03688-0_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03687-3

  • Online ISBN: 978-3-642-03688-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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