ItemArticle

Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning

Title
Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning
Creator
Umamaheswari; Kannan; Rozario, Juliet; Manimekala
Description
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.
Source
Bulletin of Electrical Engineering and Informatics;Volume;14;Issue;3;pp.1981-1989
Date
01-01-2025
Publisher
Institute of Advanced Engineering and Science
Subject
Convolutional neural networks; Deep learning; Ensemble learning; Image tampering; Particle swarm optimization
Coverage
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
Rights
All Open Access; Gold Open Access
Relation
ISSN: 20893191;
Format
online
Language
English
Type
Article

Citation

Umamaheswari; Kannan; Rozario, Juliet; Manimekala, “Exploration of digital image tampering detection using CNN with modified particle swarm optimization in deep learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23070.

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