Manifold alignment approach to cover source mismatch in steganalysis

Abstract

Cover source mismatch (CSM) is an important open problem in steganalysis. This problem, known as domain adaptation in the field of machine learning, deals with the decrease in the classification accuracy when a classifier is moved from the laboratory into the real world. In this paper, we present an approach to CSM based on domain adaptation using manifold alignment algorithms. In this novel approach, we use manifold alignment to find a latent space where the two datasets (the one used for training and the one used for testing) have a common representation. We show that manifold alignment can significantly increase the accuracy of the classifier in cross-domain classification.