Detection of Classifier Inconsistencies in Image Steganalysis
This addresses reliability issues in steganalysis for security applications, but it is incremental as it builds on existing classification-based methods.
The paper tackles the problem of unreliable predictions in image steganalysis by proposing a method to detect inconsistencies using two classifiers, showing that the number of inconsistencies can predict classification errors.
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).