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Author Identification Using Chaos Game Representation and Deep Learning

Author

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  • Catalin Stoean

    (Human Language Technology Research Center, University of Bucharest, 010014 Bucharest, Romania
    Grupo Ingeniería de Sistemas Integrados, E.T.S.I. Telecomunicación, Universidad de Málaga, 29071 Málaga, Spain)

  • Daniel Lichtblau

    (Wolfram Research, Champaign, IL 61820, USA)

Abstract

An author unconsciously encodes in the written text a certain style that is often difficult to recognize. Still, there are many computational means developed for this purpose that take into account various features, from lexical and character-based attributes to syntactic or semantic ones. We propose an approach that starts from the character level and uses chaos game representation to illustrate documents like images which are subsequently classified by a deep learning algorithm. The experiments are made on three data sets and the outputs are comparable to the results from the literature. The study also verifies the suitability of the method for small data sets and whether image augmentation can improve the classification efficiency.

Suggested Citation

  • Catalin Stoean & Daniel Lichtblau, 2020. "Author Identification Using Chaos Game Representation and Deep Learning," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1933-:d:438970
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    References listed on IDEAS

    as
    1. Efstathios Stamatatos, 2009. "A survey of modern authorship attribution methods," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 538-556, March.
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