Detection of Classifier Inconsistencies in Image Steganalysis

23 Sep 2019Daniel Lerch-HostalotDavid Megías

In this paper, a methodology to detect inconsistencies in classification-based image steganalysis is presented. The proposed approach uses two classifiers: the usual one, trained with a set formed by cover and stego images, and a second classifier trained with the set obtained after embedding additional random messages into the original training set... (read more)

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