This paper proposes to determine a sufficient number of images for reliable classification and to use feature selection to select most\r\nrelevant features for achieving reliable steganalysis. First dimensionality issues in the context of classification are outlined, and the\r\nimpact of the different parameters of a steganalysis scheme (the number of samples, the number of features, the steganography\r\nmethod, and the embedding rate) is studied. On one hand, it is shown that, using Bootstrap simulations, the standard deviation\r\nof the classification results can be very important if too small training sets are used; moreover a minimum of 5000 images is\r\nneeded in order to perform reliable steganalysis. On the other hand, we show how the feature selection process using the OP-ELM\r\nclassifier enables both to reduce the dimensionality of the data and to highlight weaknesses and advantages of the six most popular\r\nsteganographic algorithms.
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