Article: An improved continuous and discrete Harris Hawks optimiser applied to feature selection for image steganalysis Journal: International Journal of Computational Science and Engineering (IJCSE) 2024 Vol.27 No.5 pp.515 - 535 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

Title: An improved continuous and discrete Harris Hawks optimiser applied to feature selection for image steganalysis

Authors: Ankita Gupta; Rita Chhikara; Prabha Sharma

Addresses: Department of Computer Science and Engineering, The NorthCap University, Gurugram, India ' Department of Computer Science and Engineering, The NorthCap University, Gurugram, India ' Department of Computer Science and Engineering, The NorthCap University, Gurugram, India

Abstract: 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.

Keywords: steganalysis; steganography; spatial rich model; SRM; Harris Hawks optimiser; HHO; particle swarm optimisation; PSO; differential evolution; machine learning; ensemble classifier; classification.

DOI: 10.1504/IJCSE.2024.141339

International Journal of Computational Science and Engineering, 2024 Vol.27 No.5, pp.515 - 535

Received: 18 Apr 2023
Accepted: 25 Aug 2023
Published online: 09 Sep 2024 *

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Title: An improved continuous and discrete Harris Hawks optimiser applied to feature selection for image steganalysis

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