Camera-Model Identification Using Markovian Transition Probability Matrix
IWDW '09 Proceedings of the 8th International Workshop on Digital Watermarking
Accurate detection of demosaicing regularity for digital image forensics
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
The 'Dresden Image Database' for benchmarking digital image forensics
Proceedings of the 2010 ACM Symposium on Applied Computing
Image tamper detection based on demosaicing artifacts
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Source camera identification using enhanced sensor pattern noise
IEEE Transactions on Information Forensics and Security
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Blind Identification of Source Cell-Phone Model
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
Color filter arrays: design and performance analysis
IEEE Transactions on Consumer Electronics
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In this paper, we propose a camera-model classification method based on characteristics of color filter array (CFA) and interpolation. As CFA patterns and interpolation algorithms are different among different camera models, the artifacts introduced by CFA and interpolation can reflect model-specific to some extent. To capture the artifacts, we design a 69-D feature set and perform camera-model classification. Images from seven camera models in the Dresden Image Database are chosen as our experiment database. Experiment results show that in seven models detection, our method can do the classification with high detection accuracy from 98.39% to 99.88%.