Abstract
Plagiarism is the process of copying someone else’s text or figure verbatim or without due recognition of the source. A lot of techniques have been proposed for detecting plagiarism in texts, but a few techniques exist for detecting figure plagiarism. This paper focuses on detecting plagiarism in scientific figures. Existing techniques are not applicable to figures. Detecting plagiarism in figures requires extraction of information from its components to enable comparison between figures. Consequently, content-based figure plagiarism detection technique is proposed and evaluated based on the existing limitations. The proposed technique was based on the feature extraction and similarity computation methods. Feature extraction method is capable of extracting contextual features of figures in aid of understanding the components contained in figures, while similarity detection method is capable of categorizing a figure either as plagiarized or as non-plagiarized depending on the threshold value. Empirical results showed that the proposed technique was accurate and scalable.
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Acknowledgements
This work is supported by the Malaysian Ministry of Higher Education and the Research Management Centre at the Universiti Teknologi Malaysia under Research University Grant Category Vot: Q.J130000.2528.13H46.
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Eisa, T., Salim, N., Alzahrani, S. (2017). Figure Plagiarism Detection Using Content-Based Features. In: Patnaik, S., Popentiu-Vladicescu, F. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 555. Springer, Singapore. https://doi.org/10.1007/978-981-10-3779-5_3
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DOI: https://doi.org/10.1007/978-981-10-3779-5_3
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