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Source camera identification using enhanced sensor pattern noise
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Li, Chang-Tsun (2010) Source camera identification using enhanced sensor pattern noise. IEEE Transactions on Information Forensics and Security, Vol.5 (No.2). pp. 280-287. doi:10.1109/TIFS.2010.2046268
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Official URL: http://dx.doi.org/10.1109/TIFS.2010.2046268
Abstract
Sensor pattern noises (SPNs), extracted from digital images to serve as the fingerprints of imaging devices, have been proved as an effective way for digital device identification. However, as we demonstrate in this work, the limitation of the current method of extracting SPNs is that the SPNs extracted from images can be severely contaminated by details from scenes, and as a result, the identification rate is unsatisfactory unless images of a large size are used. In this work, we propose a novel approach for attenuating the influence of details from scenes on SPNs so as to improve the device identification rate of the identifier. The hypothesis underlying our SPN enhancement method is that the stronger a signal component in an SPN is, the less trustworthy the component should be, and thus should be attenuated. This hypothesis suggests that an enhanced SPN can be obtained by assigning weighting factors inversely proportional to the magnitude of the SPN components.
| Item Type: | Journal Article | ||||
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| Subjects: | T Technology > TR Photography | ||||
| Divisions: | Faculty of Science > Computer Science | ||||
| Library of Congress Subject Headings (LCSH): | Digital images, Digital cameras -- Identification, Images, Photographic -- Noise, Image processing | ||||
| Journal or Publication Title: | IEEE Transactions on Information Forensics and Security | ||||
| Publisher: | IEEE | ||||
| ISSN: | 1556-6013 | ||||
| Official Date: | June 2010 | ||||
| Dates: |
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| Volume: | Vol.5 | ||||
| Number: | No.2 | ||||
| Page Range: | pp. 280-287 | ||||
| DOI: | 10.1109/TIFS.2010.2046268 | ||||
| Status: | Peer Reviewed | ||||
| Access rights to Published version: | Open Access | ||||
| Funder: | Forensic Pathways (Firm) |
Data sourced from Thomson Reuters' Web of Knowledge
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