Towards Robust Plagiarism Detection in Programming Education: Introducing Tolerant Token Matching Techniques to Counter Novel Obfuscation Methods

With the rise of AI-generated code, programming courses face
new challenges in detecting code plagiarism. Traditional methods
struggle against obfuscation techniques that modify code structure
through statement insertion and deletion. To address this, we propose
a novel approach based on tolerant token matching designed
to enhance resilience against such attacks.We evaluate our method
through three experiments on a real-life dataset with AI-obfuscated
plagiarisms. The results show that our approach increased the median
similarity gap between originals and plagiarisms by 1 to 6
percentage points.