Image forgery detection by semi-automatic wavelet soft-Thresholding with error level analysis

2017 ◽  
Vol 85 ◽  
pp. 348-356 ◽  
Author(s):  
Daniel Cavalcanti Jeronymo ◽  
Yuri Cassio Campbell Borges ◽  
Leandro dos Santos Coelho
Keyword(s):  
Forgery Detection ◽  
Error Level ◽  
Image Forgery ◽  
Soft Thresholding ◽  
Image Forgery Detection ◽  
Level Analysis

scholarly journals Image forgery detection using error level analysis and deep learning

2018 ◽  
Vol 17 (2) ◽  
pp. 653 ◽  
Author(s):  
Ida Bagus Kresna Sudiatmika ◽  
Fathur Rahman ◽  
Trisno Trisno ◽  
Suyoto Suyoto
Keyword(s):  
Deep Learning ◽  
Forgery Detection ◽  
Error Level ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Level Analysis

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

2021 ◽  
Author(s):  
Wina Permana Sari ◽  
Hisyam Fahmi
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Extraction Process ◽  
Test Accuracy ◽  
Learning Approach ◽  
Forgery Detection ◽  
Error Level ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Level Analysis

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%.

Image Forgery Detection Based on Trigonometric Transforms

2019 ◽  
Vol 28 (2) ◽  
pp. 203-216
Author(s):  
Faten Al-Azrak ◽  
Moawad Dessouky ◽  
Fathi Abd El-Samie ◽  
Ahmed Elkorany ◽  
Zeinab Elsharkawy
Keyword(s):  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Trigonometric Transforms
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