Convolutional Neural Network for Copy-Move Forgery Detection

Autor:	Younis Abdalla, M. Tariq Iqbal, Mohamed Shehata
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	forgery detection neural networks image processing Mathematics QA1-939
Zdroj:	Symmetry, Vol 11, Iss 10, p 1280 (2019)
Druh dokumentu:	article
ISSN:	2073-8994
DOI:	10.3390/sym11101280
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/ed872247e5fe478595538fc73516b551 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.