PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Feature Selection Methodology for Steganalysis
Yoan Miche, Benoit Roue, Amaury Lendasse and Patrick Bas
In: Multimedia Content Representation, Classification and Security Lecture Notes in Computer Science , 4105 . (2006) Springer , Berlin / Heidelberg , pp. 49-56. ISBN 978-3-540-39392-4

Abstract

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.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2571
Deposited By:Amaury Lendasse
Deposited On:22 November 2006