Detection of Classifier Inconsistencies in Image Steganalysis In this paper, a methodology to detect inconsistencies in classificationbased 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).

Combining Multiple oneclass Classifiers for Anomaly based Face Spoofing Attack Detection Oneclass spoofing detection approaches have been an effective alternative to the twoclass learners in the face presentation attack detection particularly in unseen attack scenarios. We propose an ensemble based anomaly detection approach applicable to oneclass classifiers. A new score normalisation method is proposed to normalise the output of individual outlier detectors before fusion. To comply with the accuracy and diversity objectives for the component classifiers, three different strategies are utilised to build a pool of anomaly experts. To boost the performance, we also make use of the clientspecific information both in the design of individual experts as well as in setting a distinct threshold for each client. We carry out extensive experiments on three face antispoofing datasets and show that the proposed ensemble approaches are comparable superior to the techniques based on the twoclass formulation or classindependent settings. *