On the benchmarking of ResNet forgery image model using different datasets

Conference paper

Hossain, F. and Dagiuklas, A. (2022). On the benchmarking of ResNet forgery image model using different datasets. Human-Centered Cognitive Systems. Shangai, China 18 Nov - 17 Dec 2022 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/HCCS55241.2022.10090275
AuthorsHossain, F. and Dagiuklas, A.
TypeConference paper
Abstract

This paper presents the benchmarking and improve- ment of the ResNet image forgery model using three different datasets (CASIA, Columbia, and LSBU). The model is based on classification, where forgery images have been edited using cut-paste modification technique.The images are categorized to check if the algorithm can successfully identify the difference between the original and the forgery image. All images have been pre-processed with Gray-Edge detectors to obtain get better classification results. Experimental results have shown that the Gray-edge technique has improved the accuracy across all image datasets.

KeywordsImage Forgery, ResNet, LSBU-Columbia-CASIA image datasets
Year2022
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Digital Object Identifier (DOI)https://doi.org/10.1109/HCCS55241.2022.10090275
Web address (URL)http://hccs.gaasnetwork.org/
Accepted author manuscript
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Open
Publication dates
Print18 Dec 2022
Publication process dates
Accepted14 Nov 2022
Deposited29 Nov 2022
ISBN978-1-6654-5042-3
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Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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