Digital halftoning
Survey of Scanner and Printer Forensics at Purdue University
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Geometric distortion signatures for printer identification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
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
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Identifying the originating device for a given media, i.e. the type, brand, model and other characteristics of the device, is currently one of the important fields of digital forensics. This paper proposes a forensic technique based on the Benford's law to identify the printer's brand and model from the printed-and-scanned images at which the first digit probability distribution of multi-size block DCT coefficients are extracted that constitutes a feature vector as the input to support vector machine (SVM) classifier. The proposed technique is different from the traditional use of noise feature patterns appeared in the literature. It uses as few as nine numbers of forensic features representing each printer by leveraging properties of the Benford's law for printer identification. Experiments conducted over electrophotographic (EP) printers and deskjet printers achieve an average of 96.0% classification rate of identification for five distinct printer brands and an average of 94.0% classification rate for six diverse printer models out of those five brands.