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Refining PRNU-based detection of image forgeries
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Lin, Xufeng and Li, Chang-Tsun (2016) Refining PRNU-based detection of image forgeries. In: IEEE Digital Media & Academic Forum, Santorini, Greece, 4-6 Jul 2016 ISBN 9781509010004. doi:10.1109/DMIAF.2016.7574937
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Official URL: http://dx.doi.org/10.1109/DMIAF.2016.7574937
Abstract
Photo Response Non-Uniformity (PRNU) noise can be considered as a spread-spectrum watermark embedded in every image taken by the source imaging device. It has been effectively used for localizing the forgeries in digital images. The noise residual extracted from the image in question is compared with the reference PRNU in a sliding-window based manner. If their normalized cross correlation, which servers as a decision statistic, is below a pre-determined threshold (e.g., by Neyman-Pearson criterion), the center pixel in the window is declared as forged. However, the decision statistic is calculated over the forged and the non-forged regions when the sliding window falls near the boundary of the two different regions. As a result, the corresponding pixels of the forged region are probably wrongly identified as genuine ones. To alleviate this problem, we analyze the correlation distribution in the problematic region and refine the detection by weighting the decision threshold based on the altered correlation distribution. The effectiveness of the proposed refining algorithm is confirmed through the results of detecting three different kinds of realistic image forgeries.
Item Type: | Conference Item (UNSPECIFIED) | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TS Manufactures |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Data encryption (Computer science) , Image processing -- Digital techniques, Computer graphics, Watermarks, Digital images -- Watermarking | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9781509010004 | ||||||
Official Date: | September 2016 | ||||||
Dates: |
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DOI: | 10.1109/DMIAF.2016.7574937 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 18 May 2020 | ||||||
Date of first compliant Open Access: | 18 May 2020 | ||||||
RIOXX Funder/Project Grant: |
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Title of Event: | IEEE Digital Media & Academic Forum | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Santorini, Greece | ||||||
Date(s) of Event: | 4-6 Jul 2016 |
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