The effect of error level analysis on the image forgery detection using deep learning

Sari, Wina Permana ORCID: https://orcid.org/0000-0003-3289-2640 and Fahmi, Hisyam ORCID: https://orcid.org/0000-0002-2665-1536 (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). pp. 187-194. ISSN 2503-2267

Preview

Text 9757.pdf - Published Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (786kB) | Preview

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%.

Item Type:	Journal Article
Keywords:	convolutional neural network; copy-move image forgery; digital image forensics; image integrity
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Hisyam Fahmi
Date Deposited:	29 Nov 2021 13:13

Downloads

Origin of downloads

Actions (login required)

View Item