Open AccessEffect of Error Level Analysis on The Image Forgery Detection Using Deep Learning
Author(s) -
Wina Permana Sari,
Hisyam Fahmi
Publication year - 2021
Publication title -
kinetik
Language(s) - English
Resource type - Journals
eISSN - 2503-2267
pISSN - 2503-2259
DOI - 10.22219/kinetik.v6i3.1272
Subject(s) - artificial intelligence , computer science , deep learning , convolutional neural network , image (mathematics) , feature (linguistics) , computer vision , feature extraction , pattern recognition (psychology) , image quality , process (computing) , feature detection (computer vision) , image processing , philosophy , linguistics , operating system
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%.