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PCA-based denoising of Sensor Pattern Noise for source camera identification
conference contribution
posted on 2014-01-01, 00:00 authored by R Li, Y Guan, Chang-Tsun LiChang-Tsun LiSensor Pattern Noise (SPN) has been proved to be an inherent fingerprint of the imaging device for source identification. However, SPN extracted from digital images can be severely contaminated by scene details. Moreover, SPN with high dimensionality may cause excessive time cost on calculating correlation between SPNs, which will limit its applicability to the source camera identification or image classification with a large dataset. In this work, an effective scheme based on principal component analysis (PCA) is proposed to address these two problems. By transforming SPN into eigenspace spanned by the principal components, the scene details and trivial information can be significantly suppressed. In addition, due to the dimensionality reduction property of PCA, the size of SPN is greatly reduced, consequently reducing the time cost of calculating similarity between SPNs. Our experiments are conducted on the Dresden database, and results demonstrate that the proposed method outperforms could achieve the state-of-art performance in terms of the Receiver Operating Characteristic (ROC) curves while reducing the dimensionality of SPN.
History
Event
IEEE Signal Processing Society. Conference (2014 : Xi'an, China)Series
IEEE Signal Processing Society ConferencePagination
436 - 440Publisher
Institute of Electrical and Electronics EngineersLocation
Xi'an, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-07-09End date
2014-07-13ISBN-13
9781479954032Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ChinaSIP 2014 : Proceedings of the 2014 IEEE China Summit & International Conference on Signal and Information ProcessingUsage metrics
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