Evaluation of Graylevel-Features for Printing Technique Classification in High-Throughput Document Management Systems

Authors:
Christian Schulze;Marco Schreyer;Armin Stahl;Thomas M. Breuel
Affiliations:
German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern, Kaiserslautern, Germany 67663;German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern, Kaiserslautern, Germany 67663;German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern, Kaiserslautern, Germany 67663;German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern, Kaiserslautern, Germany 67663
Venue:
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Year:
2008

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Abstract

The detection of altered or forged documents is an important tool in large scale office automation. Printing technique examination can therefore be a valuable source of information to determine a questioned documents authenticity. A study of graylevel features for high throughput printing technique recognition was undertaken. The evaluation included printouts generated by 49 different laser and 13 different inkjet printers. Furthermore, the extracted document features were classified using three different machine learning approaches. We were able to show that, under the given constraints of high-throughput systems, it is possible to determine the printing technique used to create a document.