Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection

Image splicing detection is an area of studies that have been studied widely all around the world recently. The importance to do image splicing detection is not only for the authorities but also for common user. Image splicing detection requires several steps to be completed and a huge dataset is ne...

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Main Author:	Hasim, Siti Mastura Binti Md
Format:	Thesis
Language:	English
Published:	2017
Subjects:	T Technology
Online Access:	http://eprints.usm.my/52900/1/Investigation%20And%20Development%20Of%20Convolutional%20Neural%20Network%20Based%20Image%20Splicing%20Detection_Siti%20Mastura%20Binti%20Md%20Hasim_E3_2017.pdf
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id	my-usm-ep.52900
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spelling	my-usm-ep.529002022-06-15T07:10:48Z Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection 2017-08-01 Hasim, Siti Mastura Binti Md T Technology TK1-9971 Electrical engineering. Electronics. Nuclear engineering Image splicing detection is an area of studies that have been studied widely all around the world recently. The importance to do image splicing detection is not only for the authorities but also for common user. Image splicing detection requires several steps to be completed and a huge dataset is needed to be used. This study is aimed to investigate and develop CNN based method for image splicing detection. Three preliminary experiments are done according to previous work to observe how pre-processing affects CNN performance. Based on the preliminary experiments, an architecture with reduced number of CNN layers are proposed without any pre-processing. Ten-fold cross validation is used to demonstrate CNN performance. Preliminary experiments shows that CNN performance are critically affected by input image size. Therefore, the proposed architecture are tested with different input image sizes. Three different input image sizes are tested which are 28×28 pixel, 64×64 pixel and 128×128 pixels. From cross validation is can be concluded that 64×64 pixels input image is the most suitable input image size for CNN image splicing detection. At the end of this study, it is observed that by using the proposed architecture, CNN can be used for image splicing detection without any pre-processing. 2017-08 Thesis http://eprints.usm.my/52900/ http://eprints.usm.my/52900/1/Investigation%20And%20Development%20Of%20Convolutional%20Neural%20Network%20Based%20Image%20Splicing%20Detection_Siti%20Mastura%20Binti%20Md%20Hasim_E3_2017.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology T Technology
spellingShingle	T Technology T Technology Hasim, Siti Mastura Binti Md Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection
description	Image splicing detection is an area of studies that have been studied widely all around the world recently. The importance to do image splicing detection is not only for the authorities but also for common user. Image splicing detection requires several steps to be completed and a huge dataset is needed to be used. This study is aimed to investigate and develop CNN based method for image splicing detection. Three preliminary experiments are done according to previous work to observe how pre-processing affects CNN performance. Based on the preliminary experiments, an architecture with reduced number of CNN layers are proposed without any pre-processing. Ten-fold cross validation is used to demonstrate CNN performance. Preliminary experiments shows that CNN performance are critically affected by input image size. Therefore, the proposed architecture are tested with different input image sizes. Three different input image sizes are tested which are 28×28 pixel, 64×64 pixel and 128×128 pixels. From cross validation is can be concluded that 64×64 pixels input image is the most suitable input image size for CNN image splicing detection. At the end of this study, it is observed that by using the proposed architecture, CNN can be used for image splicing detection without any pre-processing.
format	Thesis
qualification_level	Master's degree
author	Hasim, Siti Mastura Binti Md
author_facet	Hasim, Siti Mastura Binti Md
author_sort	Hasim, Siti Mastura Binti Md
title	Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection
title_short	Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection
title_full	Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection
title_fullStr	Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection
title_full_unstemmed	Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection
title_sort	investigation and development of convolutional neural network based image splicing detection
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2017
url	http://eprints.usm.my/52900/1/Investigation%20And%20Development%20Of%20Convolutional%20Neural%20Network%20Based%20Image%20Splicing%20Detection_Siti%20Mastura%20Binti%20Md%20Hasim_E3_2017.pdf
_version_	1747822211123642368

Investigation And Development Of Convolutional Neural Network Based Image Splicing Detection

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