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Fusion Based Blind Image Steganalysis by Boosting Feature Selection

  • Jing Dong
  • Xiaochuan Chen
  • Lei Guo
  • Tieniu Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5041)

Abstract

In this paper, a feature-level fusion based approach is proposed for blind image steganalysis. We choose three types of typical higher-order statistics as the candidate features for fusion and make use of the Boosting Feature Selection (BFS) algorithm as the fusion tool to select a subset of these candidate features as the new fusion feature vector for blind image steganalysis. Support vector machines are then used as the classifier. Experimental results show that the fusion based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-algorithm. Moreover, we evaluate the performance of our candidate features for fusion by making some analysis of the components of the fusion feature vector in our experiments.

Keywords

Blind image steganalysis fusion boosting 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jing Dong
    • 1
  • Xiaochuan Chen
    • 1
  • Lei Guo
    • 1
  • Tieniu Tan
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

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