Adaptive image encryption approach using an enhanced swarm intelligence algorithm.

Minocha, S; Sharma, SR; Singh, B; Gandomi, AH

Adaptive image encryption approach using an enhanced swarm intelligence algorithm.

Minocha, S Sharma, SR Singh, B Gandomi, AH

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Publisher:: NATURE PORTFOLIO
Publication Type:: Journal Article
Citation:: Sci Rep, 2025, 15, (1), pp. 9476
Issue Date:: 2025-03-19

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Field	Value	Language
dc.contributor.author	Minocha, S
dc.contributor.author	Sharma, SR
dc.contributor.author	Singh, B
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2025-04-09T04:37:53Z
dc.date.available	2025-01-13
dc.date.available	2025-04-09T04:37:53Z
dc.date.issued	2025-03-19
dc.identifier.citation	Sci Rep, 2025, 15, (1), pp. 9476
dc.identifier.issn	2045-2322
dc.identifier.issn	2045-2322
dc.identifier.uri	http://hdl.handle.net/10453/186799
dc.description.abstract	Chaos-based encryption methods have gained popularity due to the unique properties of chaos. The performance of chaos-based encryption methods is highly impacted by the values of initial and control parameters. Therefore, this work proposes Iterative Cosine operator-based Hippopotamus Optimization (ICO-HO) to select optimal parameters for chaotic maps, which is further used to design an adaptive image encryption approach. ICO-HO algorithm improves the Hippopotamus Optimization (HO) by integrating a new phase (Phase 4) to update the position of the hippopotamus. ICO-HO updates the position of hippopotamuses using ICO and opposition-based learning, which enhances the exploration and exploitation capabilities of the HO algorithm. ICO-HO algorithm's better performance is signified by the Friedman mean rank test applied to mean values obtained on the CEC-2017 benchmark functions. The ICO-HO algorithm is utilized to optimize the parameters of PWLCM and PWCM chaotic maps to generate a secret key in the confusion and diffusion phases of image encryption. The performance of the proposed encryption approach is evaluated on grayscale, RGB, and hyperspectral medical images of different modalities, bit depth, and sizes. Different analyses, such as visual analysis, statistical attack analysis, differential attack analysis, and quantitative analysis, have been utilized to assess the effectiveness of the proposed encryption approach. The higher NPCR and UACI values, i.e., 99.60% and 33.40%, respectively, ensure security against differential attacks. Furthermore, the proposed encryption approach is compared with five state-of-the-art encryption techniques available in the literature and six similar metaheuristic techniques using NPCR, UACI, entropy, and correlation coefficient. The proposed methods exhibit 7.9995 and 15.8124 entropy values on 8-bit and 16-bit images, respectively, which is better than all other stated methods, resulting in improved image encryption with high randomness.
dc.format	Electronic
dc.language	eng
dc.publisher	NATURE PORTFOLIO
dc.relation.ispartof	Sci Rep
dc.relation.isbasedon	10.1038/s41598-025-86569-9
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Adaptive image encryption approach using an enhanced swarm intelligence algorithm.
dc.type	Journal Article
utslib.citation.volume	15
utslib.location.activity	England
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-04-09T04:37:13Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	15
utslib.citation.issue	1

Abstract:

Chaos-based encryption methods have gained popularity due to the unique properties of chaos. The performance of chaos-based encryption methods is highly impacted by the values of initial and control parameters. Therefore, this work proposes Iterative Cosine operator-based Hippopotamus Optimization (ICO-HO) to select optimal parameters for chaotic maps, which is further used to design an adaptive image encryption approach. ICO-HO algorithm improves the Hippopotamus Optimization (HO) by integrating a new phase (Phase 4) to update the position of the hippopotamus. ICO-HO updates the position of hippopotamuses using ICO and opposition-based learning, which enhances the exploration and exploitation capabilities of the HO algorithm. ICO-HO algorithm's better performance is signified by the Friedman mean rank test applied to mean values obtained on the CEC-2017 benchmark functions. The ICO-HO algorithm is utilized to optimize the parameters of PWLCM and PWCM chaotic maps to generate a secret key in the confusion and diffusion phases of image encryption. The performance of the proposed encryption approach is evaluated on grayscale, RGB, and hyperspectral medical images of different modalities, bit depth, and sizes. Different analyses, such as visual analysis, statistical attack analysis, differential attack analysis, and quantitative analysis, have been utilized to assess the effectiveness of the proposed encryption approach. The higher NPCR and UACI values, i.e., 99.60% and 33.40%, respectively, ensure security against differential attacks. Furthermore, the proposed encryption approach is compared with five state-of-the-art encryption techniques available in the literature and six similar metaheuristic techniques using NPCR, UACI, entropy, and correlation coefficient. The proposed methods exhibit 7.9995 and 15.8124 entropy values on 8-bit and 16-bit images, respectively, which is better than all other stated methods, resulting in improved image encryption with high randomness.

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