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Despite the effort put into the detection of academic plagiarism, itcontinues to be a ubiquitous problem spanning all disciplines. Various toolshave been developed to assist human inspectors by automatically identifyingsuspicious documents. However, to our knowledge currently none of these toolsuse mathematical content for their analysis. This is problematic, becausemathematical content potentially represents a significant amount of thescientific contribution in academic documents. Hence, ignoring mathematicalcontent limits the detection of plagiarism considerably, especially indisciplines with frequent use of mathematics.  This paper aims to help close this gap by providing an overview of existingapproaches in mathematical information retrieval and an analysis of theirapplicability for different possible cases of mathematical plagiarism. I findthat whereas syntax-based approaches perform particularly well in detectingundisguised plagiarism, structure-based and hybrid approaches promise to alsodetect forms of disguised mathematical plagiarism, such as plagiarism withrenamed identifiers. However, more research in this area is needed to enablethe detection of more complex mathematical plagiarism: the scope of currentapproaches is restricted to the formula-level, an extension to thesection-level is needed. Additionally, the general detection of equivalencetransformations is currently not feasible. Despite these remaining problems, Iconclude that the presented approaches could already be used for a basicautomated detection system targeting mathematical plagiarism and thereforeenhance current plagiarism detection systems.

Analyzing Similarity in Mathematical Content To Enhance the Detection of Academic Plagiarism