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Enhancing sensor pattern noise via filtering distortion removal

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Lin, Xufeng and Li, Chang-Tsun (2016) Enhancing sensor pattern noise via filtering distortion removal. IEEE Signal Processing Letters, 23 (3). 381 -385. doi:10.1109/LSP.2016.2521349 ISSN 1070-9908.

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Official URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?pun...

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Abstract

In this work, we propose a method to obtain higher quality sensor pattern noise (SPN) for identifying source cameras. We believe that some components of SPN have been severely contaminated by the errors introduced by denoising filters and the quality of SPN can be improved by abandoning those components. In our proposed method, some coefficients with higher denoising errors are abandoned in the wavelet representation of SPN and the remaining wavelet coefficients are further enhanced to suppress the scene details in the SPN. These two steps aim to provide better SPN with higher signalto-noise ratio (SNR) and therefore improve the identification performance. The experimental results on 2,000 images captured by 10 cameras (each responsible for 200 images), show that our method achieves better receiver operating characteristic (ROC) performance when compared with some state-of-the-art methods.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Sound analyzers
Journal or Publication Title: IEEE Signal Processing Letters
Publisher: IEEE
ISSN: 1070-9908
Official Date: March 2016
Dates:
DateEvent
March 2016Published
25 January 2016Available
20 January 2016Accepted
Volume: 23
Number: 3
Number of Pages: 5
Page Range: 381 -385
DOI: 10.1109/LSP.2016.2521349
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 26 January 2016
Date of first compliant Open Access: 26 January 2016
Funder: Seventh Framework Programme (European Commission) (FP7)
Grant number: No. 251677

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