ItemArticle

Enhanced image encryption using fractional-order chaotic systems and neural network-based optimization for secure multimedia applications

Title
Enhanced image encryption using fractional-order chaotic systems and neural network-based optimization for secure multimedia applications
Creator
P, Bhuvaneswari; V, Thirunavukkarasu; J, Loveline Zeema; NV, Poornima; Karnan, Lokanayaki
Description
The rapid expansion of multimedia data in fields like healthcare and finance necessitates robust image encryption to protect sensitive content. Conventional chaotic encryption, based on integer-order systems, is hindered by restricted key spaces (e.g., and suboptimal parameter choices, exposing vulnerabilities. This work introduces an innovative encryption method that merges a fractional-order chaotic Logistic map with neural network optimization to overcome these shortcomings and enhance security. Utilizing the Grunwald-Letnikov derivative, the fractional-order Logistic map produces a complex, unpredictable sequence for encryption. A feedforward neural network fine-tunes parameters (,), elevating the Lyapunov exponent from 0.5032 to 0.6540, signifying heightened chaos. This integration harnesses fractional-order memory effects and neural network adaptability, surpassing traditional integer-order encryption constraints. The method achieves a key space of, entropy of 7.9962, and horizontal correlation of 0.0028. Parameter sensitivity tests show significant output variation with minor changes. Security analysis yields NPCR at 99.60% and UACI at 33.45%. Neural network training achieves a low mean squared error of 0.0032912 by epoch 100, with high correlation. Encryption of 256256 images in 0.21 seconds and 720p video at 41.67 fps (0.024 s/frame) supports real-time applications. By combining fractional-order chaos with machine learning, this approach delivers superior image encryption, addressing integer-order system limitations. It provides a scalable framework for secure multimedia communications. Future efforts will extend the technique to color images and video, incorporating advanced machine learning for greater resilience. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.
Source
Multimedia Tools and Applications;Volume;85;Issue;6;Article No.;534;
Date
01-01-2026
Publisher
Springer
Subject
Fractional-order chaotic systems; Image encryption; Logistic map; Neural network optimization
Coverage
P B., Department of Machine Learning, Yazhli Global Multidisciplinary Research Organization(YGMRO), Tamilnadu, Tirupur, India; V T., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; J L.Z., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; NV P., Symbiosis Centre for Management Studies, Symbiosis International (Deemed University), Karnataka, Bengaluru, India; Karnan L., School of Computer Science and Engineering, RV University, Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 13807501; CODEN: MTAPF
Format
online
Language
English
Type
Article

Citation

P, Bhuvaneswari; V, Thirunavukkarasu; J, Loveline Zeema; NV, Poornima; Karnan, Lokanayaki, “Enhanced image encryption using fractional-order chaotic systems and neural network-based optimization for secure multimedia applications,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/21941.

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