Feature base fusion for splicing forgery detection based on neuro fuzzy

Most of image forensics researches have mainly focused on detection of artifacts introduced by a single processing tool. Thus, they have lead in the development of many specialized algorithms looking for one or more particular footprints under distinct settings. Naturally, the performance of such al...

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Main Author:	Hadigheh, Habib Ghaffari
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/50804/25/HabibGhaffariHadighehMFC2014.pdf
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spelling	my-utm-ep.508042020-07-12T06:51:25Z Feature base fusion for splicing forgery detection based on neuro fuzzy 2014-08 Hadigheh, Habib Ghaffari QA75 Electronic computers. Computer science Most of image forensics researches have mainly focused on detection of artifacts introduced by a single processing tool. Thus, they have lead in the development of many specialized algorithms looking for one or more particular footprints under distinct settings. Naturally, the performance of such algorithms are not perfect and accordingly the provided output they might be noisy, inaccurate and only partially correct. Furthermore, in practical scenarios, a forged image is often the result of utilizing several tools made available by the image-processing softwares. Therefore, reliable tamper detection requires developing several tools to deal with various tampering scenarios. Fusion of forgery detection tools based on Fuzzy Inference System has been used before for addressing this problem. Adjusting the Membership Functions and defining proper fuzzy rules for getting optimal results are a time consuming processes. This can be accounted as main disadvantage of Fuzzy Inference Systems. In this study, a Neuro Fuzzy Inference System for fusion of forgery detection tools is developed. The Neural Network characteristic of Neuro Fuzzy Inference Systems provide appropriate tool for automatically adjusting Membership Functions. Moreover, initial Fuzzy inference system is generated based on fuzzy clustering techniques. The purposed framework is implemented and validated on a benchmark image splicing dataset in which three forgery detection tools are fused based on Adaptive Neuro Fuzzy Inference System. The final outcome of the purposed method reveals that applying Neuro Fuzzy Inference systems could be a proper approach for fusion of forgery detection tools. On the best of our knowledge, this is the first time that Neuro Fuzzy Inference Systems employed for fusion of forgery detection tools. Therefore, more researches should be conducted to make it more practical and to increase the effectiveness of methodology. 2014-08 Thesis http://eprints.utm.my/id/eprint/50804/ http://eprints.utm.my/id/eprint/50804/25/HabibGhaffariHadighehMFC2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85444 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 Hadigheh, Habib Ghaffari Feature base fusion for splicing forgery detection based on neuro fuzzy
description	Most of image forensics researches have mainly focused on detection of artifacts introduced by a single processing tool. Thus, they have lead in the development of many specialized algorithms looking for one or more particular footprints under distinct settings. Naturally, the performance of such algorithms are not perfect and accordingly the provided output they might be noisy, inaccurate and only partially correct. Furthermore, in practical scenarios, a forged image is often the result of utilizing several tools made available by the image-processing softwares. Therefore, reliable tamper detection requires developing several tools to deal with various tampering scenarios. Fusion of forgery detection tools based on Fuzzy Inference System has been used before for addressing this problem. Adjusting the Membership Functions and defining proper fuzzy rules for getting optimal results are a time consuming processes. This can be accounted as main disadvantage of Fuzzy Inference Systems. In this study, a Neuro Fuzzy Inference System for fusion of forgery detection tools is developed. The Neural Network characteristic of Neuro Fuzzy Inference Systems provide appropriate tool for automatically adjusting Membership Functions. Moreover, initial Fuzzy inference system is generated based on fuzzy clustering techniques. The purposed framework is implemented and validated on a benchmark image splicing dataset in which three forgery detection tools are fused based on Adaptive Neuro Fuzzy Inference System. The final outcome of the purposed method reveals that applying Neuro Fuzzy Inference systems could be a proper approach for fusion of forgery detection tools. On the best of our knowledge, this is the first time that Neuro Fuzzy Inference Systems employed for fusion of forgery detection tools. Therefore, more researches should be conducted to make it more practical and to increase the effectiveness of methodology.
format	Thesis
qualification_level	Master's degree
author	Hadigheh, Habib Ghaffari
author_facet	Hadigheh, Habib Ghaffari
author_sort	Hadigheh, Habib Ghaffari
title	Feature base fusion for splicing forgery detection based on neuro fuzzy
title_short	Feature base fusion for splicing forgery detection based on neuro fuzzy
title_full	Feature base fusion for splicing forgery detection based on neuro fuzzy
title_fullStr	Feature base fusion for splicing forgery detection based on neuro fuzzy
title_full_unstemmed	Feature base fusion for splicing forgery detection based on neuro fuzzy
title_sort	feature base fusion for splicing forgery detection based on neuro fuzzy
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2014
url	http://eprints.utm.my/id/eprint/50804/25/HabibGhaffariHadighehMFC2014.pdf
_version_	1747817538576711680

Feature base fusion for splicing forgery detection based on neuro fuzzy

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