Title: Hybrid grasshopper and ant lion algorithms to improve imperceptibility, robustness, and convergence rate for the video steganography

Authors: Sahil Gupta; Naresh Kumar Garg

Addresses: Department of Electronics and Communication Engineering, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India ' Department of Computer Science and Engineering, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

Abstract: The need for securing multimedia content from being intercepted is a prominent research issue. This work proposes an optimised video steganography model that improves imperceptibility and robustness by extracting keyframes and calculating the optimal scaling factor. The squirrel search algorithm (SSA) is used to extract keyframes since it ensures distinct position updation processes through Levy flying and predator features, whilst the grasshopper optimisation and ant lion optimisation algorithms are hybridised to compute the optimal value of the scaling factor. In terms of imperceptibility and robustness, the simulation results suggest that the proposed approach outperforms existing data-hiding models. It also discovers the optimal scaling factor in under ten iterations, indicating that the fastest convergence rate is possible.

Keywords: ant lion optimisation; ALO; grasshopper optimisation; singular value decomposition; SVD; video steganography; imperceptibility; robustness; PSNR; mean square error; MSE; squirrel search algorithm; SSA.

DOI: 10.1504/IJCSE.2023.131510

International Journal of Computational Science and Engineering, 2023 Vol.26 No.3, pp.324 - 336

Received: 14 Oct 2021
Received in revised form: 19 Apr 2022
Accepted: 27 Apr 2022
Published online: 15 Jun 2023 *

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