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
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
Web address (URL)http://hccs.gaasnetwork.org/
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Print18 Dec 2022
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Accepted14 Nov 2022
Deposited29 Nov 2022
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