Image Forgery Detection Based on the Convolutional Neural Network

2020 ◽  
Author(s):  
Feng Guorui ◽  
Wu Jian
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

Image Forgery Detection Based on Motion Blur Estimated Using Convolutional Neural Network

2019 ◽  
Vol 19 (23) ◽  
pp. 11601-11611 ◽  
Author(s):  
Chunhe Song ◽  
Peng Zeng ◽  
Zhongfeng Wang ◽  
Tong Li ◽  
Lin Qiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motion Blur ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

Analysis of Image Forgery Detection using Convolutional Neural Network

2021 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Bunil Kumar Balabantaray ◽  
Chiluveru Gnaneshwar ◽  
Satyendra Singh Yadav ◽  
Manish Kumar Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

Image Forgery: Detection of Manipulated Images Using Neural Network

2020 ◽  
Author(s):  
Nitin Kumar ◽  
Padmesh Naik ◽  
Nikhil Raina ◽  
Deepali Kayande
Keyword(s):  
Neural Network ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection

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.

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