Preprocessing reference sensor pattern noise via spectrum equalization

Lin, Xufeng and Li, Chang-Tsun 2016, Preprocessing reference sensor pattern noise via spectrum equalization, IEEE transactions on information forensics and security, vol. 11, no. 1, pp. 126-140, doi: 10.1109/TIFS.2015.2478748.

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Title Preprocessing reference sensor pattern noise via spectrum equalization
Author(s) Lin, Xufeng
Li, Chang-TsunORCID iD for Li, Chang-Tsun
Journal name IEEE transactions on information forensics and security
Volume number 11
Issue number 1
Start page 126
End page 140
Total pages 15
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2016-01
ISSN 1556-6013
Keyword(s) Multimedia forensics
source camera identification (SCI)
sensor pattern noise
spectrum equalization
Science & Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Summary Although sensor pattern noise (SPN) has been proved to be an effective means to uniquely identify digital cameras, some non-unique artifacts, shared among cameras undergo the same or similar in-camera processing procedures, often give rise to false identifications. Therefore, it is desirable and necessary to suppress these unwanted artifacts so as to improve the accuracy and reliability. In this paper, we propose a novel preprocessing approach for attenuating the influence of the non-unique artifacts on the reference SPN to reduce the false identification rate. Specifically, we equalize the magnitude spectrum of the reference SPN through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts. Combined with six SPN extractions or enhancement methods, our proposed spectrum equalization algorithm is evaluated on the Dresden image database as well as our own database, and compared with the state-of-the-art preprocessing schemes. The experimental results indicate that the proposed procedure outperforms, or at least performs comparable with, the existing methods in terms of the overall receiver operating characteristic curves and kappa statistic computed from a confusion matrix, and tends to be more resistant to JPEG compression for medium and small image blocks.
Language eng
DOI 10.1109/TIFS.2015.2478748
Field of Research 08 Information And Computing Sciences
09 Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2015, IEEE
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