Publication
Document Authentication using Printing Technique Features and Unsupervised Anomaly Detection
Johann Gebhardt; Markus Goldstein; Faisal Shafait; Andreas Dengel
In: Proceedings of the 12th International Conference on Document Analysis and Recognition. International Conference on Document Analysis and Recognition (ICDAR-2013), 12th, August 25-28, Washington, DC, USA, Pages 479-483, ISBN 978-0-7695-4999-3, IEEE Computer Society, 8/2013.
Abstract
Automatically identifying that a certain page in a set of documents is printed with a different printer than the rest of the documents can give an important clue for a possible forgery attempt. Different printers vary in their produced printing quality, which is especially noticeable at the edges of printed characters. In this paper, a system using the difference in edge roughness to distinguish laser printed pages from inkjet printed pages is presented. Several feature extraction methods have been developed and evaluated for that purpose. In contrast to previous work, this system uses unsupervised anomaly detection to detect documents printed by a different printing technique than the majority of the documents among a set. This approach has the advantage that no prior training using genuine documents has to be done. Furthermore, we created a dataset featuring 1200 document images from different domains (invoices, contracts, scientific papers) printed by 7 different inkjet and 13 laser printers. Results show that the presented feature extraction method achieves the best outlier rank score in comparison to state-of-the-art features.