An Improved Continuous and Discrete Harris Hawks Optimiser Applied to Feature Selection for Image Steganalysis

To attack advanced steganography, high-dimensional rich feature set such as spatial rich model (SRM) (34671-dimensional) is extracted for image steganalysis. To address the dimensionality curse, researchers utilised feature selection techniques and developed efficient steganalysers. In this study, the Harris Hawks optimiser (HHO) is combined with particle swarm optimisation (PSO) and differential evolution (DE) to increase the exploitation and exploration capabilities of HHO respectively. This hybridised HHO is called DEHHPSO and gives good results on continuous optimisation problems as well as on feature selection problems. Initially, the Fisher filter method is used to discard some irrelevant features and the resultant features are passed to the proposed DEHHPSO feature selection method. The combined approach removes more than 94% features of the SRM feature set with improved detection accuracy when compared with state-of-the-art methods. The classification performance using the selected features is also superior to the several deep learning networks of steganalysis.