Plagiarism detection of figure images in scientific publications

Taiseer Abdalla Elfadil Eisa

International Journal of Data Mining, Modelling and Management, 2022, vol. 14, issue 1, 15-29

Abstract: Plagiarism is stealing others' work using their words directly or indirectly without a credit citation. Copying others' ideas is another type of plagiarism that may occur in many areas but the most serious one is the academic plagiarism. Therefore, technical solutions are urgently required for automatic detection of idea plagiarism. Detection of figure plagiarism is a particularly challenging field of research, because not only the text analytics but also graphic features need to be analysed. This paper investigates the issues of idea and figure plagiarism and proposes a detection method which copes with both text and structure change. The procedure depends on finding similar semantic meanings between figures by applying image processing and semantic mapping techniques. The figures were compared using the representation of shape features based on detailed comparisons between the components of figures. This is an improvement over existing methods, which only compare the numbers and types of shapes inside figures.

Keywords: plagiarism detection; figure plagiarism detection; idea plagiarism detection; academic plagiarism; image processing; semantic mapping techniques; content-based algorithms. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=122036 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:14:y:2022:i:1:p:15-29

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().