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.
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Acknowledgments
This work is supported by the National Science Foundation of China (No. 61502076) and the Scientific Research Project of Liaoning Provincial Education Department (No. L2015114).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, Y., Wang, B., Chong, K., Guo, Y. (2018). Complexity Based Sample Selection for Camera Source Identification. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_17
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DOI: https://doi.org/10.1007/978-3-319-73564-1_17
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