Image forensic for digital image copy move forgery detection

In recent years, digital image forgery detection has become an active research area due to the advancement of photo editing software. In general, image forgery detection can be classified into two types, namely active and passive detection. Active forgery detection relies on embedded authentication...

Full description

Saved in:

Bibliographic Details
Main Author:	Yeap, Yong Yew
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/79086/1/YeapYongYewMFKE2018.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.79086
record_format	uketd_dc
spelling	my-utm-ep.790862018-09-27T06:07:28Z Image forensic for digital image copy move forgery detection 2018-01 Yeap, Yong Yew TK Electrical engineering. Electronics Nuclear engineering In recent years, digital image forgery detection has become an active research area due to the advancement of photo editing software. In general, image forgery detection can be classified into two types, namely active and passive detection. Active forgery detection relies on embedded authentication code in the image while passive forgery detection relies solely on the images for authentication. The forgery detection techniques are used to identify images tampered with common techniques such as copy move, slicing, contrast alteration and sharpening/blurring. This project focuses on passive forgery detection on images tampered using copy move technique, better known as Copy Move Forgery Detection (CMFD). A CMFD technique consisting of oriented Features from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features (Oriented FAST and rotated BRIEF) as the feature extraction method and 2 Nearest Neighbour (2NN) with Hierachical Agglomerative Clustering (HAC) as the feature matching method is proposed. The ORB parameters, namely the number of features to retain and patch size are optimized using Particle Swarm Optimization (PSO). The optimization is essential in obtaining a balance between performance and runtime. Evaluation of the proposed CMFD technique is performed on images which underwent various geometrical attacks. With the proposed technique, an overall accuracy rate of 84.33% and 82.79% is obtained for evaluation carried out with images from the MICC-F600 and MICC-F2000 databases. Forgery detection is performed accurately, with True Positive Rate of 91% and above, for tampered images with object translation, different degree of rotation and enlargement. However, the performance degraded for tampered images with reduced copied object size and asymmetrical scaling, with True Positive Rate of 73.68% and 38.15% respectively. 2018-01 Thesis http://eprints.utm.my/id/eprint/79086/ http://eprints.utm.my/id/eprint/79086/1/YeapYongYewMFKE2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:108133 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Yeap, Yong Yew Image forensic for digital image copy move forgery detection
description	In recent years, digital image forgery detection has become an active research area due to the advancement of photo editing software. In general, image forgery detection can be classified into two types, namely active and passive detection. Active forgery detection relies on embedded authentication code in the image while passive forgery detection relies solely on the images for authentication. The forgery detection techniques are used to identify images tampered with common techniques such as copy move, slicing, contrast alteration and sharpening/blurring. This project focuses on passive forgery detection on images tampered using copy move technique, better known as Copy Move Forgery Detection (CMFD). A CMFD technique consisting of oriented Features from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features (Oriented FAST and rotated BRIEF) as the feature extraction method and 2 Nearest Neighbour (2NN) with Hierachical Agglomerative Clustering (HAC) as the feature matching method is proposed. The ORB parameters, namely the number of features to retain and patch size are optimized using Particle Swarm Optimization (PSO). The optimization is essential in obtaining a balance between performance and runtime. Evaluation of the proposed CMFD technique is performed on images which underwent various geometrical attacks. With the proposed technique, an overall accuracy rate of 84.33% and 82.79% is obtained for evaluation carried out with images from the MICC-F600 and MICC-F2000 databases. Forgery detection is performed accurately, with True Positive Rate of 91% and above, for tampered images with object translation, different degree of rotation and enlargement. However, the performance degraded for tampered images with reduced copied object size and asymmetrical scaling, with True Positive Rate of 73.68% and 38.15% respectively.
format	Thesis
qualification_level	Master's degree
author	Yeap, Yong Yew
author_facet	Yeap, Yong Yew
author_sort	Yeap, Yong Yew
title	Image forensic for digital image copy move forgery detection
title_short	Image forensic for digital image copy move forgery detection
title_full	Image forensic for digital image copy move forgery detection
title_fullStr	Image forensic for digital image copy move forgery detection
title_full_unstemmed	Image forensic for digital image copy move forgery detection
title_sort	image forensic for digital image copy move forgery detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2018
url	http://eprints.utm.my/id/eprint/79086/1/YeapYongYewMFKE2018.pdf
_version_	1747818143357599744

Image forensic for digital image copy move forgery detection

Similar Items