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Adaptive image encryption approach using an enhanced swarm intelligence algorithm.

Sachin Minocha1, Suvita Rani Sharma1, Birmohan Singh2

  • 1School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India.

Scientific Reports
|March 20, 2025
PubMed
Summary

This study introduces an Iterative Cosine operator-based Hippopotamus Optimization (ICO-HO) algorithm for optimizing chaotic map parameters in image encryption. The novel approach enhances security and randomness for medical images, outperforming existing methods.

Keywords:
ChaoticHippopotamus optimizationHyperspectral imageImage encryptionIterative Cosine OperatorMedical imagePWCMPWLCM

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Area of Science:

  • Cryptography
  • Computer Science
  • Artificial Intelligence

Background:

  • Chaos-based encryption leverages unique chaotic properties for secure data transmission.
  • Optimal selection of initial and control parameters is crucial for the performance of chaos-based encryption methods.
  • Existing optimization techniques may lack sufficient exploration and exploitation capabilities for complex parameter spaces.

Purpose of the Study:

  • To propose an Iterative Cosine operator-based Hippopotamus Optimization (ICO-HO) algorithm for optimizing chaotic map parameters.
  • To design an adaptive image encryption approach utilizing the optimized chaotic maps.
  • To enhance the security and performance of image encryption, particularly for medical imaging applications.

Main Methods:

  • Development of the ICO-HO algorithm, integrating a novel Phase 4 for position updates and employing opposition-based learning.
  • Optimization of parameters for PWLCM and PWCM chaotic maps using ICO-HO for key generation in image encryption.
  • Evaluation of the proposed encryption approach on diverse medical images (grayscale, RGB, hyperspectral) using visual, statistical, differential, and quantitative analyses.

Main Results:

  • The ICO-HO algorithm demonstrated superior performance on CEC-2017 benchmark functions compared to standard Hippopotamus Optimization (HO).
  • The proposed encryption method achieved high NPCR (99.60%) and UACI (33.40%) values, indicating strong resistance to differential attacks.
  • The method achieved excellent entropy values (7.9995 for 8-bit, 15.8124 for 16-bit images), surpassing state-of-the-art techniques.

Conclusions:

  • The ICO-HO algorithm effectively optimizes chaotic map parameters for robust image encryption.
  • The proposed adaptive image encryption approach offers enhanced security and high randomness, suitable for sensitive medical data.
  • ICO-HO represents a significant advancement in metaheuristic optimization for cryptographic applications.