Secured forensic image analysis by optimised iterative model with random consensus approaches
by S.B. Gurumurthy; Ajit Danti
International Journal of Computational Science and Engineering (IJCSE), Vol. 28, No. 2, 2025

Abstract: 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 RSC's 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.

Online publication date: Mon, 03-Mar-2025

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