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.
Maisch et al. (Thu,) studied this question.