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 Umamaheswari; Kannan; Rozario, Juliet; Manimekala Title A name given to the resource Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning 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 Bulletin of Electrical Engineering and Informatics;Volume;14;Issue;3;pp.1981-1989 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/eei.v14i3.9400" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/eei.v14i3.9400</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005260385?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005260385?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 Umamaheswari, Department of Computer Science, Christ University, Bangalore, India; Kannan, Department of Computer Science, Christ University, Bangalore, India; Rozario J., Department of Computer Science, Christ University, Bangalore, India; Manimekala, Department of Computer Science, Christ University, Bangalore, India Description An account of the resource The field of image processing is crucial for many different applications, including forensic evidence, insurance claims, medical imaging, bio-informatics, artifact collection and more. In many sectors nowadays, digital photographs are regarded as a trustworthy source of information. The manipulation of such photographs leads to a variety of issues. The study presents a method using convolutional neural networks (CNN) combined with modified particle swarm optimization (MPSO) to improve the accuracy of tampering detection. This advancement contributes to improved reliability in fields requiring image authenticity verification, such as forensics and media. The design includes the collection of a dataset comprising both original and tampered images for training and testing the model. A dataset, such as the Media Integration and Communication Center (MICC) dataset, is utilized, which includes various images that have been altered through different tampering techniques. This dataset serves as the foundation for training the CNN and evaluating its performance The findings indicate that the proposed MPSO_CNN method outperforms traditional techniques in terms of precision, accuracy, recall, and F-measure, demonstrating its effectiveness in identifying tampered images. The results highlight the significance of using advanced deep learning techniques for reliable image authenticity verification. 2025, Institute of Advanced Engineering and Science. All rights reserved. Subject The topic of the resource Convolutional neural networks; Deep learning; Ensemble learning; Image tampering; Particle swarm optimization Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Relation A related resource ISSN: 20893191; 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 All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online