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Vo. 6, No. 3, August 2021

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The Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning

https://doi.org/10.22219/kinetik.v6i3.1272
Wina Permana Sari
Bina Nusantara University, Jakarta
Hisyam Fahmi
Universitas Islam Negeri Malik Maulana Ibrahim Malang

Corresponding Author(s) : Hisyam Fahmi

hisyam.fahmi@uin-malang.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vo. 6, No. 3, August 2021

Abstract

Digital image modification or image forgery is easy to do today. The authenticity verification of an image become important to protect the image integrity so that the image is not being misused. Error Level Analysis (ELA) can be used to detect the modification in image by lowering the quality of image and comparing the error level. The use of deep learning approach is a state-of-the-art in solving cases of image data classification. This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. The impacts of applying different ELA compression levels, such as 10, 50, and 90 percent, were also compared in this study. According to the results, adopting the ELA feature increases validation accuracy by about 2.7% and give the better test accuracy. However, the use of ELA will slow down the processing time by about 5.6%.

Keywords

convolutional neural network copy-move image forgery digital image forensics image integrity
Sari, W. P., & Fahmi, H. (2021). The Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 6(3). https://doi.org/10.22219/kinetik.v6i3.1272
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Author Biography

Wina Permana Sari, Bina Nusantara University, Jakarta

Computer Science Department, School of Computer Science

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