Title: Content-Based Scientific Figure Plagiarism Detection Using Semantic Mapping
Abstract: Plagiarism is to steal 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. Academic misconduct forms high-profile plagiarism cases at universities. Therefore, technical solutions are strictly demanded for automatic idea plagiarism detection. Detection of figure plagiarism is a challenge field of research because not only the text analytics but also graphic features are analyzed. This paper investigates the issue of idea and figure plagiarism and proposes a detection method which copes with text and structure change. The procedure depends on finding similar semantic meanings between figures by applying image processing and semantic mapping techniques.
Publication Year: 2019
Publication Date: 2019-11-02
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
Cited By Count: 6
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