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Source camera identification using footprints from lens aberration

Authors: Kai San Choi; Edmund Y. Lam; Kenneth K. Y. Wong;

Source camera identification using footprints from lens aberration

Abstract

Source camera identification is the process of discerning which camera has been used to capture a particular image. In this paper, we consider the more fundamental problem of trying to classify images captured by a limited number of camera models. Inspired by the previous work that uses sensor imperfection, we propose to use the intrinsic lens aberration as features in the classification. In particular, we focus on lens radial distortion as the primary distinctive feature. For each image under investigation, parameters from pixel intensities and aberration measurements are obtained. We then employ a classifier to identify the source camera of an image. Simulation is carried out to evaluate the success rate of our method. The results show that this is a viable procedure in source camera identification with a high probability of accuracy. Comparing with the procedures using only image intensities, our approach improves the accuracy from 87% to 91%.

Country
China (People's Republic of)
Related Organizations
Keywords

Image processing, Source camera identification, Lens aberration, Statistical classification, Forensic science

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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
92
Top 10%
Top 1%
Top 10%
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