On the benchmarking of ResNet forgery image model using different datasets
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
|Hossain, F. and Dagiuklas, A.
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.
|Image Forgery, ResNet, LSBU-Columbia-CASIA image datasets
|Institute of Electrical and Electronics Engineers (IEEE)
|Digital Object Identifier (DOI)
|Web address (URL)
|Accepted author manuscript
File Access Level
|18 Dec 2022
|Publication process dates
|14 Nov 2022
|29 Nov 2022
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