Same Same But Different: Preventing Refactoring Attacks on Software Plagiarism Detection
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909875556057088 |
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| author | Maisch, Robin Schmid, Larissa Sağlam, Timur Niehues, Nils |
| author_facet | Maisch, Robin Schmid, Larissa Sağlam, Timur Niehues, Nils |
| contents | Plagiarism detection in programming education faces growing challenges due to increasingly sophisticated obfuscation techniques, particularly automated refactoring-based attacks. While code plagiarism detection systems used in education practice are resilient against basic obfuscation, they struggle against structural modifications that preserve program behavior, especially caused by refactoring-based obfuscation. This paper presents a novel and extensible framework that enhances state-of-the-art detectors by leveraging code property graphs and graph transformations to counteract refactoring-based obfuscation. Our comprehensive evaluation of real-world student submissions, obfuscated using both algorithmic and AI-based obfuscation attacks, demonstrates a significant improvement in detecting plagiarized code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Same Same But Different: Preventing Refactoring Attacks on Software Plagiarism Detection Maisch, Robin Schmid, Larissa Sağlam, Timur Niehues, Nils Software Engineering K.3.2; K.6.5; K.4.1 Plagiarism detection in programming education faces growing challenges due to increasingly sophisticated obfuscation techniques, particularly automated refactoring-based attacks. While code plagiarism detection systems used in education practice are resilient against basic obfuscation, they struggle against structural modifications that preserve program behavior, especially caused by refactoring-based obfuscation. This paper presents a novel and extensible framework that enhances state-of-the-art detectors by leveraging code property graphs and graph transformations to counteract refactoring-based obfuscation. Our comprehensive evaluation of real-world student submissions, obfuscated using both algorithmic and AI-based obfuscation attacks, demonstrates a significant improvement in detecting plagiarized code. |
| title | Same Same But Different: Preventing Refactoring Attacks on Software Plagiarism Detection |
| topic | Software Engineering K.3.2; K.6.5; K.4.1 |
| url | https://arxiv.org/abs/2510.25057 |