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Steganalysis by Ensemble Classifiers with Boosting by Regression, and Post-Selection of Features

Marc Chaumont 1 Sarra Kouider 1
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper we extend the state-of-the-art steganalysis tool developed by Kodovsky and Fridrich: the Kodovsky's ensem-ble classifiers. We propose to boost the weak classifiers com-posing the Kodovsky classifier. For this, we minimize the probability of error thanks to a regression approach of low complexity. We also propose a post-selection of features, achieved after the learning step of all the weak classifiers. For each weak classifier, we identify a subset of features reducing the probability of error. Both proposals are of neg-ligeable complexity compared to the complexity of the Kodovsky classifier. Moreover, these two proposals significantly increase the performance of classification.
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Submitted on : Monday, July 1, 2013 - 6:50:25 PM
Last modification on : Thursday, May 24, 2018 - 3:59:23 PM
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Marc Chaumont, Sarra Kouider. Steganalysis by Ensemble Classifiers with Boosting by Regression, and Post-Selection of Features. ICIP: International Conference on Image Processing, Sep 2012, Orlando, FL, United States. pp.1133-1136, ⟨10.1109/ICIP.2012.6467064⟩. ⟨lirmm-00838995⟩

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