A Feature Selection Methodology for Steganalysis

  • Yoan Miche
  • Benoit Roue
  • Amaury Lendasse
  • Patrick Bas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


This paper presents a methodology to select features before training a classifier based on Support Vector Machines (SVM). In this study 23 features presented in [1] are analysed. A feature ranking is performed using a fast classifier called K-Nearest-Neighbours combined with a forward selection. The result of the feature selection is afterward tested on SVM to select the optimal number of features. This method is tested with the Outguess steganographic software and 14 features are selected while keeping the same classification performances. Results confirm that the selected features are efficient for a wide variety of embedding rates. The same methodology is also applied for Steghide and F5 to see if feature selection is possible on these schemes.


Support Vector Machine Feature Selection Feature Selection Method Stego Image Noise Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yoan Miche
    • 1
  • Benoit Roue
    • 2
  • Amaury Lendasse
    • 1
  • Patrick Bas
    • 1
    • 2
  1. 1.Laboratory of Computer and Information ScienceHelsinki University of TechnologyHutFinland
  2. 2.Laboratoire des Images et des Signaux de GrenobleSaint Martin d’HèresFrance

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