HyPlag is a prototype of a hybrid plagiarism detection system developed by the Chair for Data & Knowledge Engineering at the University of Wuppertal.
Compared to existing approaches for plagiarism detection, the presented prototype does not consider textual similarity alone but uses citation patterns within academic documents, mathematical content and images as unique, language-independent fingerprints to identify semantic similarity. The combined analysis of text and text-independent features for the first time enables the automated detection of strongly disguised plagiarism forms, such as paraphrases, translated plagiarism, and idea plagiarism.
While the hybrid approach to plagiarism detection offers unique benefits, it should be seen as a supplement not a replacement to existing text-based plagiarism detection software.
For more information on the analysis approaches, publications and press coverage, see the HyPlag project page.
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