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 Gurumurthy, S.B.; Danti, Ajit Title A name given to the resource Secured forensic image analysis by optimised iterative model with random consensus approaches Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Computational Science and Engineering;Volume;28;Issue;2;pp.172-184 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJCSE.2025.144801" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJCSE.2025.144801</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000511655?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000511655?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 Gurumurthy S.B., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Danti A., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India Description An account of the resource Future measurements, software, and scalability testing related to cloud performance are required for forensic image scalability (FIS) optimisations and advancements. An advanced iterative reconstruction model and consensus mechanism must be used to quantitatively evaluate image quality in any blockchain framework since this will have a direct impact on the security and usability of the framework. This work addresses these problems by presenting a fast and efficient forgery detection system based on optimal security, feature extraction, and pre-processing. This will render conventional media security and forensic techniques meaningless. In this work, a random sample consensus (RSC) method is proposed for the analysis of FIS. To ensure that the architecture is as strong and secure as possible, the iterative reconstruction model (IRM) is employed. Initially, one may consider channel processing to be a form of database picture pre-processing. One perspective states that the enhanced chicken swarm optimisation (ECSO) algorithm is used to advance the scaling settings to balance invisibility and power. This RSCs threshold setting reduces the number of excluded matches as well as the root mean square error (RMSE). Enhancement of scalability as well as picture reconstruction demonstrate the utility of the proposed technology. The simulation findings on multiple retinal image datasets demonstrate that the proposed method further enhances accuracy matching by 10.56% and rate of progress by 30% on average compared with the RSC-IRM strategy. 2025 Inderscience Enterprises Ltd. Subject The topic of the resource forensic image; image reconstruction; image security; optimisation; scalability Publisher An entity responsible for making the resource available Inderscience Publishers Relation A related resource ISSN: 17427185; 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