Feature reduction of rich features for universal steganalysis using a metaheuristic approach

Abstract

The development of content adaptive steganographies has become a challenge for steganalysis. This led researchers towards extraction of a rich space of features. The detection of stego images based on spatial rich model (SRM) features and its variants is a promising research area in the field of universal steganalysis. SRM features are extracted as 106 sub-models which collectively provide 34,671 features. So, one of the most significant challenges in universal steganalysis is feature selection. In this paper an improved binary particle swarm optimisation, global and local best particle swarm optimisation (GLBPSO) with Fisher linear discriminant classifier is used to identify relevant feature sub-models which improve the efficiency of a steganalyser. The significant reduction rate of more than 70% is achieved by the proposed approach. This further helps in reducing computational complexity without much affecting the detection capability. The proposed methodology gives superior results when compared with state-of-the-art algorithms.