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...

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Main Author: Yeap, Yong Yew
Format: Thesis
Language:English
Published: 2018
Subjects:
Online Access:http://eprints.utm.my/id/eprint/79086/1/YeapYongYewMFKE2018.pdf
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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