HyPlag

Abstract

Current plagiarism detection systems reliably find instances of copied and moderately altered text, but often fail to detect strong paraphrases, translations, and the reuse of non-textual content and ideas. To improve upon the detection capabilities for such concealed content reuse in academic publications, we make four contributions: i) We present the first plagiarism detection approach that combines the analysis of mathematical expressions, images, citations and text. ii) We describe the implementation of this hybrid detection approach in the research prototype HyPlag. iii) We present novel visualization and interaction concepts to aid users in reviewing content similarities identified by the hybrid detection approach. iv) We demonstrate the usefulness of the hybrid detection and result visualization approaches by using HyPlag to analyze a confirmed case of content reuse present in a retracted research publication.

Detecting Machine-Obfuscated Plagiarism

“…Approaches analyzing nontextual content features, such as academic citations [16,28], images [25,11], and mathematical content [26,29], complement the text analysis approaches to improve the detection of concealed plagiarism.…”

Forms of Plagiarism in Digital Mathematical Libraries

“…However, only a few studies have addressed the detection of plagiarism in digital mathematical libraries [19,21,22] regardless of the detection approach. We briefly describe the main findings of these studies hereafter.…”

“…In a follow-up study, Meuschke et al (2019) introduced similarity measures that consider the order of mathematical identifiers and presented a two-stage retrieval process consisting of a candidate retrieval and a detailed analysis stage that replaced the exclusive use of pairwise document comparisons [22]. They implemented the process in the HyPlag prototype that also offers a user interface to investigate the identified similarities [21]. The candidate retrieval stage employs efficient index-based retrieval methods based on mathematical features.…”

The Sequence Labeling Approach for Text Alignment of Plagiarism Detection

“…The exact matches are usually identified based on the features of character-based or word-based n-gram. The same character n-grams [5][6][7] or word n-grams [8][9][10][11][12] can be viewed as a pair of exact plagiarism match. The creating matches mainly rely on the features of text similarity.…”