Printer identification using supervised learning for document forgery detection

S. Elkasrawi, Faisal Shafait

    Research output: Chapter in Book/Conference paperConference paper

    22 Citations (Scopus)

    Abstract

    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%. © 2014 IEEE.
    Original languageEnglish
    Title of host publicationDocument Analysis Systems (DAS), 2014 11th IAPR International Workshop on Document Analysis Systems
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages146-150
    VolumeN/A
    ISBN (Print)9781479932436
    DOIs
    Publication statusPublished - 2014
    Event11th IAPR International Workshop on Document Analysis Systems - France, Tours, France
    Duration: 7 Apr 201410 Apr 2014
    Conference number: 106059

    Workshop

    Workshop11th IAPR International Workshop on Document Analysis Systems
    CountryFrance
    CityTours
    Period7/04/1410/04/14

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    Cite this

    Elkasrawi, S., & Shafait, F. (2014). Printer identification using supervised learning for document forgery detection. In Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on Document Analysis Systems (Vol. N/A, pp. 146-150). [6830987] USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DAS.2014.48