Printer Identification Using Supervised Learning for Document Forgery Detection

Sarah Elkasrawi; Faisal Shafait
In: 11th IAPR International Workshop on Document Analysis Systems. IAPR International Workshop on Document Analysis Systems (DAS-2014), April 7-10, Tours, France, Pages 146-150, ISBN 978-1-4799-3243-6, IEEE, 2014.


Identifying the source printer of a document is important in forgery detection. The larger the number of documents to be investigated for forgery, the less time-efficient manual examination becomes. Assuming the document in question was scanned, the accuracy of automatic forgery detection depends on the scanning resolution. Low (100-200 dpi) and common (300-400 dpi) resolution scans have less distinctive features than high-end scanner resolution, whereas the former is more widespread in offices. In this paper, we propose a method to automatically identify source printers using common-resolution scans (400 dpi). Our method depends on distinctive noise produced by printers. Independent of the document content or size, each printer produces noise depending on its printing technique, brand and slight differences due to manufacturing imperfections. Experiments were carried out on a set of 400 documents of similar structure printed using 20 different printers. The documents were scanned at 400 dpi using the same scanner. Assuming constant settings of the printer, the overall accuracy of the classification was 76.75%.

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