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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
http://eprints.utm.my/id/eprint/48037/
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spelling my.utm.480372017-08-02T02:50:24Z http://eprints.utm.my/id/eprint/48037/ Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/48037/25/RoyaEsmaeilaniMFK2014.pdf Esmaeilani, Roya (2014) Source identification of captured video using photo response non-uniformity noise pattern and svm classifiers. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computing.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2014
url http://eprints.utm.my/id/eprint/48037/25/RoyaEsmaeilaniMFK2014.pdf
http://eprints.utm.my/id/eprint/48037/
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score 13.211869

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