On the benchmarking of ResNet forgery image model using different datasets

Faria Hossain; Tasos Dagiuklas

doi:10.1109/HCCS55241.2022.10090275

On the benchmarking of ResNet forgery image model using different datasets

Faria Hossain, Tasos Dagiuklas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665450416
DOIs	https://doi.org/10.1109/HCCS55241.2022.10090275 https://doi.org/10.1109/HCCS55241.2022.10090275
Publication status	Published - 18 Dec 2022
Event	2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022 - Shanghai, China Duration: 17 Dec 2022 → 18 Dec 2022

Publication series

Name	Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022

Conference

Conference	2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022
Country/Territory	China
City	Shanghai
Period	17/12/22 → 18/12/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Image Forgery
LSBU-Columbia-CASIA image datasets
ResNet

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On the benchmarking of ResNet forgery image model using three different datasets (1)[13]Accepted author manuscript, 920 KBLicence: CC BY 4.0

Cite this

Hossain, F., & Dagiuklas, T. (2022). On the benchmarking of ResNet forgery image model using different datasets. In Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022 (Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HCCS55241.2022.10090275, https://doi.org/10.1109/HCCS55241.2022.10090275

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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.",

keywords = "Image Forgery, LSBU-Columbia-CASIA image datasets, ResNet",

author = "Faria Hossain and Tasos Dagiuklas",

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Hossain, F & Dagiuklas, T 2022, On the benchmarking of ResNet forgery image model using different datasets. in Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022. Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022, Shanghai, China, 17/12/22. https://doi.org/10.1109/HCCS55241.2022.10090275, https://doi.org/10.1109/HCCS55241.2022.10090275

On the benchmarking of ResNet forgery image model using different datasets. / Hossain, Faria; Dagiuklas, Tasos.
Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Dagiuklas, Tasos

PY - 2022/12/18

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022

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ER -

Hossain F, Dagiuklas T. On the benchmarking of ResNet forgery image model using different datasets. In Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings - 2022 International Conference on Human-Centered Cognitive Systems, HCCS 2022). doi: 10.1109/HCCS55241.2022.10090275, 10.1109/HCCS55241.2022.10090275

On the benchmarking of ResNet forgery image model using different datasets

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