Exploring feature coupling and model coupling for image source identification

Huang, Y; Cao, L; Zhang, J; Pan, L; Liu, Y

Exploring feature coupling and model coupling for image source identification

Huang, Y Cao, L

Zhang, J Pan, L Liu, Y

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2018, 13 (12), pp. 3108 - 3121
Issue Date:: 2018-12-01

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Field	Value	Language
dc.contributor.author	Huang, Y	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Pan, L	en_US
dc.contributor.author	Liu, Y	en_US
dc.date.issued	2018-12-01	en_US
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2018, 13 (12), pp. 3108 - 3121	en_US
dc.identifier.issn	1556-6013	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131873
dc.description.abstract	© 2005-2012 IEEE. Recently, there has been great interest in feature-based image source identification. Previous statistical learning-based methods usually regarded the identification process as a classification problem. They assumed the dependence of features and the dependence of models. However, the two assumptions are usually problematic because of the genuine coupling of features and models. To address the issues, in this paper, we propose a novel image source identification scheme. For the feature coupling, a coupled feature representation is adopted to analyze the coupled interaction among features. The coupling relations among features and their powers are measured with Pearson's correlations and integrated in a Taylor-like expansion manner. Regarding model coupling, a new coupled probability representation is developed. The model coupling relationships are characterized with conditional probabilities induced by the confusion matrix and then combined with the law of total probability. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed scheme. Via mining the feature coupling and model coupling, the identification accuracy can be significantly improved.	en_US
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security	en_US
dc.relation.isbasedon	10.1109/TIFS.2018.2838079	en_US
dc.subject.classification	Strategic, Defence & Security Studies	en_US
dc.title	Exploring feature coupling and model coupling for image source identification	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	13	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	13	en_US

Abstract:

© 2005-2012 IEEE. Recently, there has been great interest in feature-based image source identification. Previous statistical learning-based methods usually regarded the identification process as a classification problem. They assumed the dependence of features and the dependence of models. However, the two assumptions are usually problematic because of the genuine coupling of features and models. To address the issues, in this paper, we propose a novel image source identification scheme. For the feature coupling, a coupled feature representation is adopted to analyze the coupled interaction among features. The coupling relations among features and their powers are measured with Pearson's correlations and integrated in a Taylor-like expansion manner. Regarding model coupling, a new coupled probability representation is developed. The model coupling relationships are characterized with conditional probabilities induced by the confusion matrix and then combined with the law of total probability. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed scheme. Via mining the feature coupling and model coupling, the identification accuracy can be significantly improved.

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http://hdl.handle.net/10453/131873