MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Intrinsic sensor noise features for forensic analysis on scanners and scanned images
IEEE Transactions on Information Forensics and Security
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Determining Image Origin and Integrity Using Sensor Noise
IEEE Transactions on Information Forensics and Security
Nonintrusive Component Forensics of Visual Sensors Using Output Images
IEEE Transactions on Information Forensics and Security
Building fingerprints with information from three color bands for source camera identification
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
Digital image forensics: a booklet for beginners
Multimedia Tools and Applications
Detection of tampering inconsistencies on mobile photos
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Analysis of multimedia forensic technique
Proceedings of the 2011 International Conference on Communication, Computing & Security
Measuring the statistical correlation inconsistencies in mobile images for tamper detection
Transactions on Data Hiding and Multimedia Security VII
Camera model identification based on the characteristic of CFA and interpolation
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Identification of smartphone-image source and manipulation
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Open set source camera attribution and device linking
Pattern Recognition Letters
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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.