  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. When the
decisions of these two classifiers are not consistent, we know that the
prediction is not reliable. The number of inconsistencies in the predictions of
a testing set may indicate that the classifier is not performing correctly in
the testing scenario. This occurs, for example, in case of cover source
mismatch, or when we are trying to detect a steganographic method that the
classifier is no capable of modelling accurately. We also show how the number
of inconsistencies can be used to predict the reliability of the classifier
(classification errors).
