scholarly journals Methods of Detecting Image forgery using convolutional neural network

2021 ◽  
Vol 1831 (1) ◽  
pp. 012026
Author(s):  
Alka Leekha ◽  
Arpan Gupta ◽  
Amit Kumar ◽  
Tarun Chaudhary
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
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.

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

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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