Modified moth flame optimization and logistic chaotic map integration for image encryption

Akshat Aggarwal, Eshaan Awasthi, Deepika Kukreja (), Jyoti Kedia and Indu Bala
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Akshat Aggarwal: Netaji Subhas University of Technology
Eshaan Awasthi: Netaji Subhas University of Technology
Deepika Kukreja: Netaji Subhas University of Technology
Jyoti Kedia: Punjab Engineering College (Deemed to be University)
Indu Bala: SEEE, Lovely Professional University

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 2, No 20, 785-804

Abstract: Abstract Preserving the confidentiality of sensitive image data holds paramount significance in the realm of digital communication. Hence, this paper introduces an optimized image encryption algorithm aimed at enhancing the security of image-based communication. The proposed algorithm, known as the Modified Moth Flame Optimization Algorithm (MMFO), is employed in conjunction with a Logistic Chaotic Map to yield improved values for evaluation parameters such as Correlation Coefficient (CC), Image Entropy (IE), Unified Average Changing Intensity (UACI), and Normalized Pixel Change Rate (NPCR). These enhancements are achieved alongside the attainment of Uniform Histograms for the encrypted image, surpassing the performance of various state-of-the-art metaheuristic algorithms integrated with diverse chaotic maps. Multiple plain image-dependent session keys, in conjunction with a Logistic Chaotic map, are utilized to generate several encrypted images, serving as an initial population for the Modified algorithm. The presented results validate the efficacy of the proposed algorithm, demonstrating a notable de-correlation of adjacent pixels in the encrypted image, with achieved values on the order of $$10^{-6}$$ 10 - 6 .

Keywords: Image encryption; Metaheuristic Algorithms; Moth Flame Optimization; Chaotic Maps; Correlation Coefficient (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-024-02669-1

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