Exploring multitask learning for steganalysis

This paper introduces a new technique for multi-actor steganalysis. In conventional settings, it is unusual for one actor to generate enough data to be able to train a personalized classifier. On the other hand, in a network there will be many actors, between them generating large amounts of data. Prior work has pooled the training data, and then tries to deal with its heterogeneity. In this work, we use multitask learning to account for differences between actors' image sources, while still sharing domain (globally-applicable) information. We tackle the problem by learning separate feature weights for each actor, and sharing information between the actors through the regularization. This way, the domain information that is obtained by considering all actors at the same time is not disregarded, but the weights are nevertheless personalized. This paper explores whether multitask learning improves accuracy of detection, by benchmarking new multitask learners against previous work. © 2013 SPIE-IS&T.