Adaptive image encryption approach using an enhanced swarm intelligence algorithm

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-03-19
DOI
https://doi.org/10.1038/s41598-025-86569-9
Citations
3
Primary Topic
Chaos-based Image/Signal Encryption
Type
article
Field-Weighted Citation Impact
3.83

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adaptive image encryption approach using an enhanced swarm intelligence algorithm

Suvita Rani Sharma, Amir H. Gandomi, Birmohan Singh, Sachin Minocha
3 citations
Scientific Reports
Chaos-based Image/Signal Encryption
3.83
article

Adaptive image encryption approach using an enhanced swarm intelligence algorithm

Suvita Rani Sharma, Amir H. Gandomi, Birmohan Singh, Sachin Minocha
article en
3 citations

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.

Scientific ReportsVol. 15(1)
Obuda University (HU), Khazar University (AZ), University of Technology Sydney (AU), Bennett University (IN), Sant Longowal Institute of Engineering and Technology (IN)
Óbudai Egyetem
Openalex Percentile: Top 7%
Chaos-based Image/Signal Encryption
3.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.