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

  • Jing Dong
  • Wei Wang
  • Tieniu Tan
Conference paper

DOI: 10.1007/978-3-642-03688-0_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5703)
Cite this paper as:
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

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.

Keywords

blind steganalysis multi-class image steganalysis run-length analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jing Dong
    • 1
  • Wei Wang
    • 1
  • Tieniu Tan
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijing

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