Research Article
Complexity Based Sample Selection for Camera Source Identification
@INPROCEEDINGS{10.1007/978-3-319-73564-1_17, author={Yabin Li and Bo Wang and Kun Chong and Yanqing Guo}, title={Complexity Based Sample Selection for Camera Source Identification}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Camera source identification Sensor pattern noise Sample selection Image complexity}, doi={10.1007/978-3-319-73564-1_17} }- Yabin Li
Bo Wang
Kun Chong
Yanqing Guo
Year: 2018
Complexity Based Sample Selection for Camera Source Identification
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_17
Abstract
Sensor patter noise (SPN) has been proved to be an unique fingerprint of a camera, and widely used for camera source identification. Previous works mostly construct reference SPN by averaging the noise residuals extracted from images like blue sky. However, this is unrealistic in practice and the noise residual would be seriously affected by scene detail, which would significantly influence the performance of camera source identification. To address this problem, a complexity based sample selection method is proposed in this paper. The proposed method is adopted before the extraction of noise residual to select image patches with less scene detail to generate the reference SPN. An extensive comparative experiments show its effectiveness in eliminating the influence of image content and improving the identification accuracy of the existing methods.