Image Content Forgery Detection Model Combining PSO and SVM in Electronic Data Forensics
DOI:
https://doi.org/10.31449/inf.v48i8.5897Abstract
With the rapid progress of information technology, the Internet is saturated with copious amounts of data and visuals. However, with the widespread availability of different image editing software, counterfeit image content arises periodically. To tackle image content forgery, the research is founded on the Gaussian mixture distribution similarity measure image forgery detection algorithm. The image classifier underwent training through encoding its underlying features and utilizing the encoded data as inputs for the Support Vector Machine. Optimization of the Support Vector Machine was performed simultaneously using the Improved Particle Swarm Optimization algorithm. The results indicate that the SVM-based image content forgery detection model, which employed improved Particle Swarm Optimization, achieved a detection rate of 94.89% and processed the images in 22.06 milliseconds. In summary, the study of an image content forgery detection model that combines improved Particle Swarm Optimization and Support Vector Machine in electronic data forensics has resulted in a high detection accuracy.
Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
