Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource P, Bhuvaneswari; V, Thirunavukkarasu; J, Loveline Zeema; NV, Poornima; Karnan, Lokanayaki Title A name given to the resource Enhanced image encryption using fractional-order chaotic systems and neural network-based optimization for secure multimedia applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Multimedia Tools and Applications;Volume;85;Issue;6;Article No.;534; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11042-026-21701-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11042-026-21701-z</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039884419?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039884419?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Fractional-order chaotic systems; Image encryption; Logistic map; Neural network optimization Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 13807501; CODEN: MTAPF Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online