Scanner identification using feature-based processing and analysis

Authors:
Nitin Khanna;Aravind K. Mikkilineni;Edward J. Delp
Affiliations:
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN;School of Mechanical Engineering, Purdue University, West Lafayette, IN;School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN
Venue:
IEEE Transactions on Information Forensics and Security
Year:
2009

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Abstract

Digital images can be obtained through a variety of sources including digital cameras and scanners. In many cases, the ability to determine the source of a digital image is important. This paper presents methods for authenticating images that have been acquired using flatbed desktop scanners. These methods use scanner fingerprints based on statistics of imaging sensor pattern noise. To capture different types of sensor noise, a denoising filter-bank consisting four different denoising filters is used for obtaining the noise patterns. To identify the source scanner, a support vector machine classifier based on these fingerprints is used. These features are shown to achieve high classification accuracy. Furthermore, the selected fingerprints based on statistical properties of the sensor noise are shown to be robust under postprocessing operations, such as JPEG compression, contrast stretching, and sharpening.