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The FLD ensemble classifier is a widely used machine learning tool for steganalysis of digital media due to its efficiency when working with high dimensional feature sets. This paper explains how this classifier can be formulated within the framework of optimal detection by using an accurate statistical model of base learners' projections and the hypothesis testing theory. A substantial advantage of this formulation is the ability to theoretically establish the test properties, including thedoi:10.1109/wifs.2014.7084322 dblp:conf/wifs/CogranneDF14 fatcat:pbmsx3tc3rafxftg4gpy2j5eii