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Automated Detection of AI-Obfuscated Plagiarism in Modeling Assignments

Sağlam, Timur ORCID iD icon 1; Hahner, Sebastian ORCID iD icon 1; Schmid, Larissa ORCID iD icon 1; Burger, Erik ORCID iD icon 1
1 Institut für Informationssicherheit und Verlässlichkeit (KASTEL), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Plagiarism is a widespread problem in computer science education, exacerbated by the impracticability of manual inspection in large courses. Even worse, tools based on large language models like ChatGPT have made it easier than ever to obfuscate plagiarized solutions. Additionally, most plagiarism detectors only apply to code, and only a few approaches exist for modeling assignments, which lack broad resilience to obfuscation attacks. This paper presents a novel approach for automated plagiarism detection in modeling assignments that combines automated analysis with human inspection. We evaluate our approach with real-world assignments and plagiarism obfuscated by ChatGPT. Our results show that we achieve a significantly higher detection rate for AI-generated attacks and a broader resilience than the state-of-the-art.


Postprint §
DOI: 10.5445/IR/1000167587
Veröffentlicht am 23.01.2024
Originalveröffentlichung
DOI: 10.1145/3639474.3640084
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2024
Sprache Englisch
Identifikator ISBN: 979-8-4007-0498-7
KITopen-ID: 1000167587
HGF-Programm 46.23.03 (POF IV, LK 01) Engineering Security for Mobility Systems
Weitere HGF-Programme 46.23.01 (POF IV, LK 01) Methods for Engineering Secure Systems
Erschienen in Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training, ICSE SEET ’24, Lissabon, 14th-20th April 2024
Veranstaltung 46th International Conference on Software Engineering (ICSE 2024), Lissabon, Portugal, 14.04.2024 – 20.04.2024
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Bemerkung zur Veröffentlichung in press
Schlagwörter Software Plagiarism Detection, Plagiarism Obfuscation, Obfuscation Attacks, Modeling Plagiarism, ChatGPT, Artificial Intelligence
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