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Color-decoupled photo response non-uniformity for digital image forensics
The last few years have seen the use of photo response non-uniformity noise (PRNU), a unique fingerprint of imaging sensors, in various digital forensic applications such as source device identification, content integrity verification, and authentication. However, the use of a color filter array for capturing only one of the three color components per pixel introduces color interpolation noise, while the existing methods for extracting PRNU provide no effective means for addressing this issue. Because the artificial colors obtained through the color interpolation process are not directly acquired from the scene by physical hardware, we expect that the PRNU extracted from the physical components, which are free from interpolation noise, should be more reliable than that from the artificial channels, which carry interpolation noise. Based on this assumption we propose a couple-decoupled PRNU (CD-PRNU) extraction method, which first decomposes each color channel into four sub-images and then extracts the PRNU noise from each sub-image. The PRNU noise patterns of the sub-images are then assembled to get the CD-PRNU. This new method can prevent the interpolation noise from propagating into the physical components, thus improving the accuracy of device identification and image content integrity verification.
History
Journal
IEEE transactions on circuits and systems for video technologyVolume
22Issue
2Pagination
260 - 271Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
ISSN
1051-8215Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2011, IEEEUsage metrics
Keywords
Color filter arraydemosaicingimage authenticationimage forensicsphoto response non-uniformity noise (PRNU)source device identificationScience & TechnologyTechnologyEngineering, Electrical & ElectronicEngineeringCAMERA IDENTIFICATIONWATERMARKINGAUTHENTICATIONDEMOSAICKINGFORGERIESTRACESArtificial Intelligence and Image Processing
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