A pixel-based digital photo authentication framework via demosaicking inter-pixel correlation
Proceedings of the 11th ACM workshop on Multimedia and security
Using Sensor Noise to Identify Low Resolution Compressed Videos from YouTube
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
Intrinsic sensor noise features for forensic analysis on scanners and scanned images
IEEE Transactions on Information Forensics and Security
Accurate detection of demosaicing regularity for digital image forensics
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
A bibliography on blind methods for identifying image forgery
Image Communication
Detection of tampering inconsistencies on mobile photos
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Computer Standards & Interfaces
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
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The various image-processing stages in a digital camera pipeline leave telltale footprints, which can be exploited as forensic signatures. These footprints consist of pixel defects, of unevenness of the responses in the charge-coupled device sensor, black current noise, and may originate from proprietary interpolation algorithms involved in color filter array. Various imaging device (camera, scanner, etc.) identification methods are based on the analysis of these artifacts. In this paper, we set to explore three sets of forensic features, namely binary similarity measures, image-quality measures, and higher order wavelet statistics in conjunction with SVM classifiers to identify the originating camera. We demonstrate that our camera model identification algorithm achieves more accurate identification, and that it can be made robust to a host of image manipulations. The algorithm has the potential to discriminate camera units within the same model.