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Open AccessArticle

Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network

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Electrical Engineering& Applied Science, Memorial University, St, John’s, NL A1C 5S7 Canada
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Department of Computer Science, Math, Physics, and Statistics, University of British Columbia, Kelowna, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Information 2019, 10(9), 286; https://doi.org/10.3390/info10090286
Received: 19 August 2019 / Revised: 6 September 2019 / Accepted: 6 September 2019 / Published: 16 September 2019
(This article belongs to the Section Information and Communications Technology)
The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN. View Full-Text
Keywords: image forgery; copy-move forgery; CNN; convolutional layer; GAN; neural network training image forgery; copy-move forgery; CNN; convolutional layer; GAN; neural network training
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Abdalla, Y.; Iqbal, M.T.; Shehata, M. Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network. Information 2019, 10, 286.

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