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Most work on steganalysis, except a few exceptions, have primarily focused on providing features with high discrimination power without giving due consideration to issues concerning practical deployment of steganalysis methods. In this work, we focus on machine learning aspect of steganalyzer design and utilize a hierarchical ensemble of classifiers based approach to tackle two main issues. Firstly, proposed approach provides a workable and systematic procedure to incorporate severaldoi:10.1109/icpr.2010.1064 dblp:conf/icpr/BayramDSM10 fatcat:kvggbfe7szb7lniwwpybaqicae