@conference {18282,
	title = {Semi non-intrusive training for cell-phone camera model linkage},
	booktitle = {Information Forensics and Security (WIFS), 2010 IEEE International Workshop on},
	year = {2010},
	month = {2010/12//},
	pages = {1 - 6},
	abstract = {This paper presents a study of cell-phone camera model linkage that matches digital images against potential makes / models of cell-phone camera sources using camera color interpolation features. The matching performance is examined and the dependency on the content of training image collection is evaluated via variance analysis. Training content dependency can be dealt with under the framework of component forensics, where cell-phone camera model linkage is seen as a combination of semi non-intrusive training and completely non-intrusive testing. Such a viewpoint suggests explicitly the goodness criterion of testing accuracy for training data selection. It also motivates other possible alternative training procedures based on different criteria, such as the training complexity, for which preliminary but promising experiment designs and results have been obtained.},
	keywords = {accuracy;training, analysis;cameras;cellular, analysis;image, camera, Color, colour, complexity;training, content, dependency;variance, feature;cell, forensics;digital, forensics;image, image, Interpolation, linkage;component, matching;interpolation;, matching;semi, model, nonintrusive, phone, radio;computer, training;testing},
	doi = {10.1109/WIFS.2010.5711468},
	author = {Chuang,Wei-Hong and M. Wu}
}
@conference {18224,
	title = {Image acquisition forensics: Forensic analysis to identify imaging source},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {1657 - 1660},
	abstract = {With widespread availability of digital images and easy-to-use image editing softwares, the origin and integrity of digital images has become a serious concern. This paper introduces the problem of image acquisition forensics and proposes a fusion of a set of signal processing features to identify the source of digital images. Our results show that the devices{\textquoteright} color interpolation coefficients and noise statistics can jointly serve as good forensic features to help accurately trace the origin of the input image to its production process and to differentiate between images produced by cameras, cell phone cameras, scanners, and computer graphics. Further, the proposed features can also be extended to determining the brand and model of the device. Thus, the techniques introduced in this work provide a unified framework for image acquisition forensics.},
	keywords = {ACQUISITION, acquisition;image, analysis;, analysis;image, analysis;interpolation;statistical, cameras;color, cell, coefficients;computer, colour, editing, forensics;image, graphics;digital, identification;noise, images;forensic, Interpolation, phone, processing;data, softwares;imaging, source, statistics;scanners;signal},
	doi = {10.1109/ICASSP.2008.4517945},
	author = {McKay,C. and Swaminathan,A. and Gou,Hongmei and M. Wu}
}
