Open AccessAutomatic source camera identification using the intrinsic lens radial distortion
Author(s) -
Kai San Choi,
Edmund Y. Lam,
Kenneth K. Wong
Publication year - 2006
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.14.011551
Subject(s) - artificial intelligence , computer vision , computer science , zoom lens , camera lens , zoom , digital camera , distortion (music) , lens (geology) , identification (biology) , camera auto calibration , camera resectioning , optics , bandwidth (computing) , physics , amplifier , computer network , botany , biology
Source camera identification refers to the task of matching digital images with the cameras that are responsible for producing these images. This is an important task in image forensics, which in turn is a critical procedure in law enforcement. Unfortunately, few digital cameras are equipped with the capability of producing watermarks for this purpose. In this paper, we demonstrate that it is possible to achieve a high rate of accuracy in the identification by noting the intrinsic lens radial distortion of each camera. To reduce manufacturing cost, the majority of digital cameras are equipped with lenses having rather spherical surfaces, whose inherent radial distortions serve as unique fingerprints in the images. We extract, for each image, parameters from aberration measurements, which are then used to train and test a support vector machine classifier. We conduct extensive experiments to evaluate the success rate of a source camera identification with five cameras. The results show that this is a viable approach with high accuracy. Additionally, we also present results on how the error rates may change with images captured using various optical zoom levels, as zooming is commonly available in digital cameras.