Source printer classification using printer specific local texture descriptor

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dc.contributor.author Joshi, Sharad
dc.contributor.author Khanna, Nitin
dc.date.accessioned 2019-08-14T12:22:35Z
dc.date.available 2019-08-14T12:22:35Z
dc.date.issued 2019-05
dc.identifier.citation Joshi, Sharad and Khanna, Nitin, "Source printer classification using printer specific local texture descriptor", IEEE Transactions on Information Forensics and Security (TIFS), DOI: 10.1109/TIFS.2019.2919869, vol. 15, pp. 160-171, May 2019. en_US
dc.identifier.issn 1556-6013
dc.identifier.uri https://doi.org/10.1109/TIFS.2019.2919869
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/4645
dc.description.abstract The knowledge of source printer can help in printed text document authentication, copyright ownership, and provide important clues about the author of a fraudulent document along with his/her potential means and motives. Development of automated systems for classifying printed documents based on their source printer, using image processing techniques, is gaining a lot of attention in multimedia forensics. Currently, state-of-the-art systems require that the font of letters present in test documents of unknown origin must be available in those used for training the classifier. In this work, we attempt to take the first step towards overcoming this limitation. Specifically, we introduce a novel printer specific local texture descriptor. The highlight of our technique is the use of encoding and regrouping strategy based on small linear-shaped structures composed of pixels having similar intensity and gradient. The results of experiments performed on two separate datasets show that: 1) on a publicly available dataset, the proposed method outperforms state-of-the-art algorithms for characters printed in the same font and reduces the confusion between the printers of same brand and model, and 2) on another dataset1 having documents printed in four different fonts, the proposed method outperforms state-of-the-art methods for cross font experiments
dc.description.statementofresponsibility by Sharad Joshi and Nitin Khanna
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.subject Printers en_US
dc.subject Feature extraction en_US
dc.subject Printing en_US
dc.subject Support vector machines en_US
dc.subject Discrete wavelet transforms en_US
dc.subject Training en_US
dc.subject Distortion en_US
dc.title Source printer classification using printer specific local texture descriptor en_US
dc.type Article en_US
dc.relation.journal IEEE Transactions on Information Forensics and Security


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