Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers

Recent works have shown that passive capturing source detection methods based on Photo-Response-Non-Uniformity (PRNU) extraction are the most reliable ones in comparison with techniques that based on lens properties or compression artifacts. Some important issues in this field include: employing an...

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Main Author:	Esmaeilani, Roya
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/48037/25/RoyaEsmaeilaniMFK2014.pdf
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spelling	my-utm-ep.480372017-08-02T02:50:24Z Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers 2014-06 Esmaeilani, Roya QA75 Electronic computers. Computer science Recent works have shown that passive capturing source detection methods based on Photo-Response-Non-Uniformity (PRNU) extraction are the most reliable ones in comparison with techniques that based on lens properties or compression artifacts. Some important issues in this field include: employing an effective method for extracting PRNU, calculating the similarity and categorizing videos according to source of camera. In this study, a comprehensive algorithm is proposed to compare and evaluate the performance of different source detection methods in terms of filters used and partitioning process applied for PRNU extraction coupled with SVM classifier. Moreover, in consideration of observations, a new method is proposed for sampling selection using SVM classifier. Furthermore, the capabilities of employing and combining the results of different color parts of videos are used instead of changing them to grayscale. The proposed algorithm is based on three essential steps: Firstly, fingerprint of each camera, which is regarded as reference PRNU, is calculated by extracting PRNU of blue-sky videos. Secondly, the PRNU similarities of sample videos with reference PRNU are measured by calculating cross correlation and Peak to Correlation Energy (PCE) metrics. Finally, the sample videos are classified based on calculated PCE with SVM classifier. Experimental results revealed that Zero-mean and Wiener filters have small influences on PRNU, thus they can be ignored. Experimental results also revealed that eliminating the partitioning step considerably increases the performance of detection success rate by 15%. Among SVM classifiers, “RBF” and “MLP” types have the best identification rate of 75%. 2014-06 Thesis http://eprints.utm.my/id/eprint/48037/ http://eprints.utm.my/id/eprint/48037/25/RoyaEsmaeilaniMFK2014.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Esmaeilani, Roya Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers
description	Recent works have shown that passive capturing source detection methods based on Photo-Response-Non-Uniformity (PRNU) extraction are the most reliable ones in comparison with techniques that based on lens properties or compression artifacts. Some important issues in this field include: employing an effective method for extracting PRNU, calculating the similarity and categorizing videos according to source of camera. In this study, a comprehensive algorithm is proposed to compare and evaluate the performance of different source detection methods in terms of filters used and partitioning process applied for PRNU extraction coupled with SVM classifier. Moreover, in consideration of observations, a new method is proposed for sampling selection using SVM classifier. Furthermore, the capabilities of employing and combining the results of different color parts of videos are used instead of changing them to grayscale. The proposed algorithm is based on three essential steps: Firstly, fingerprint of each camera, which is regarded as reference PRNU, is calculated by extracting PRNU of blue-sky videos. Secondly, the PRNU similarities of sample videos with reference PRNU are measured by calculating cross correlation and Peak to Correlation Energy (PCE) metrics. Finally, the sample videos are classified based on calculated PCE with SVM classifier. Experimental results revealed that Zero-mean and Wiener filters have small influences on PRNU, thus they can be ignored. Experimental results also revealed that eliminating the partitioning step considerably increases the performance of detection success rate by 15%. Among SVM classifiers, “RBF” and “MLP” types have the best identification rate of 75%.
format	Thesis
qualification_level	Master's degree
author	Esmaeilani, Roya
author_facet	Esmaeilani, Roya
author_sort	Esmaeilani, Roya
title	Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers
title_short	Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers
title_full	Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers
title_fullStr	Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers
title_full_unstemmed	Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers
title_sort	source identification of captured video using photo response non-uniformity noise pattern and svm classifiers
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/48037/25/RoyaEsmaeilaniMFK2014.pdf
_version_	1747817292433981440

Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers

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