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Lookup NU author(s): Dr Yu GuanORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2017 The Authors Sensor pattern noise (SPN) is an inherent fingerprint of imaging devices, which provides an effective way for source camera identification (SCI). Although SPNs extracted from large image blocks usually yield high identification accuracy, their high dimensionality would incur a high computational cost in the matching stage, consequently hindering many applications that require efficient camera matchings. In this work, we employ and evaluate the concept of principal component analysis (PCA) de-noising in SCI tasks. Based on this concept, we present a framework that formulates a compact SPN representation. To enhance the de-noising effect, we introduce a training set construction procedure that minimizes the impact of various interfering artifacts, which is especially useful in some challenging cases, e.g., when only textured reference images are available. To further boost the SCI performance, a novel approach based on linear discriminant analysis (LDA) is adopted to extract more discriminant SPN features. To evaluate our methods, extensive experiments are conducted on the Dresden image database. The results indicate that the proposed framework can serve as an effective post-processing procedure, which not only boosts the performance, but also greatly reduces the computational cost in the matching phase.
Author(s): Li R, Li C-T, Guan Y
Publication type: Article
Publication status: Published
Journal: Pattern Recognition
Year: 2018
Volume: 74
Pages: 556-567
Print publication date: 01/02/2018
Online publication date: 27/09/2017
Acceptance date: 18/09/2017
Date deposited: 07/11/2017
ISSN (print): 0031-3203
ISSN (electronic): 1873-5142
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.patcog.2017.09.027
DOI: 10.1016/j.patcog.2017.09.027
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