Digital image forgery detection using artificial neural network and independent component analysis

2007 ◽  
Vol 194 (2) ◽  
pp. 540-543 ◽  
Author(s):  
E.S. Gopi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Independent Component Analysis ◽  
Digital Image ◽  
Component Analysis ◽  
Independent Component ◽  
Forgery Detection ◽  
Image Forgery ◽  
Artificial Neural ◽  
Digital Image Forgery

scholarly journals Convolutional Neural Network for Copy-Move Forgery Detection

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1280 ◽  
Author(s):  
Younis Abdalla ◽  
M. Iqbal ◽  
Mohamed Shehata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Digital Image ◽  
Forgery Detection ◽  
Image Forgery ◽  
Available Technology ◽  
Copy Move Forgery Detection ◽  
Digital Image Forgery

Digital image forgery is a growing problem due to the increase in readily-available technology that makes the process relatively easy. In response, several approaches have been developed for detecting digital forgeries. This paper proposes a novel scheme based on neural networks and deep learning, focusing on the convolutional neural network (CNN) architecture approach to enhance a copy-move forgery detection. The proposed approach employs a CNN architecture that incorporates pre-processing layers to give satisfactory results. In addition, the possibility of using this model for various copy-move forgery techniques is explained. The experiments show that the overall validation accuracy is 90%, with a set iteration limit.

Sign in / Sign up

Export Citation Format

Share Document