Timur Sağlam | Searcharxiv

arXiv2025

Same Same But Different: Preventing Refactoring Attacks on Software Plagiarism Detection

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.

cs.SE↗

arXiv2025

Evaluating Software Plagiarism Detection in the Age of AI: Automated Obfuscation and Lessons for Academic Integrity

Plagiarism in programming assignments is a persistent issue in computer science education, increasingly complicated by the emergence of automated obfuscation attacks. While software plagiarism detectors are widely used to identify suspicious similarities at scale and are resilient to simple obfuscation techniques, they are vulnerable to advanced obfuscation based on structural modification of program code that preserves the original program behavior. While different defense mechanisms have been proposed to increase resilience against these attacks, their current evaluation is limited to the scope of attacks used and lacks a comprehensive investigation regarding AI-based obfuscation. In this paper, we investigate the resilience of these defense mechanisms against a broad range of automated obfuscation attacks, including both algorithmic and AI-generated methods, and for a wide variety of real-world datasets. We evaluate the improvements of two defense mechanisms over the plagiarism detector JPlag across over four million pairwise program comparisons. Our results show significant improvements in detecting obfuscated plagiarism instances, and we observe an improved detection of AI-generated programs, even though the defense mechanisms are not designed for this use case. Based on our findings, we provide an in-depth discussion of their broader implications for academic integrity and the role of AI in education.

cs.SE↗