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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 Li
Sensor 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.



IEEE Signal Processing Society. Conference (2014 : Xi'an, China)


IEEE Signal Processing Society Conference


436 - 440


Institute of Electrical and Electronics Engineers


Xi'an, China

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E1.1 Full written paper - refereed

Copyright notice

2014, IEEE



Title of proceedings

ChinaSIP 2014 : Proceedings of the 2014 IEEE China Summit & International Conference on Signal and Information Processing