li-inferenceofaconcept-2018.pdf (1.4 MB)
Inference of a compact representation of sensor fingerprint for source camera identification
journal contribution
posted on 2018-02-01, 00:00 authored by R Li, Chang-Tsun LiChang-Tsun Li, Y GuanSensor 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.
History
Journal
Pattern recognitionVolume
74Pagination
556 - 567Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
Link to full text
ISSN
0031-3203Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2017, The AuthorsUsage metrics
Categories
Keywords
Image forensicsSource camera identification (SCI)Sensor pattern noise (SPN)PCA de-noisingScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringPHOTO RESPONSE NONUNIFORMITYPCAInformation SystemsMechanical EngineeringArtificial Intelligence and Image ProcessingDistributed Computing
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