A Feature Selection Methodology for Steganalysis
Yoan Miche, Benoit Roue, Amaury Lendasse and Patrick Bas
Multimedia Content Representation, Classification and Security
Lecture Notes in Computer Science
, Berlin / Heidelberg
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  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.