Bio inspired optimization for universal spatial image steganalysis

Abstract

Universal Image steganalysis is a two class optimization problem. This research uses S-UNIWARD spatial steganographic algorithm to create stego images from 500 cover images. The image features extracted in spatial domain are noise models of neighbouring pixels giving 1000×34671 features. Ant Lion Optimization (ALO) is used to get best image features (1000×381 features). The classifiers used are Single (SVM and MLP) and Fusion classifiers (Bayes, Decision Template, Dempster Schafer). All fusion classifiers and SVM give classification accuracy of 99.3%. Thus Fusion classifiers with ALO act as best universal steganalyser in spatial domain.