Mitigating Obfuscation Attacks on Software Plagiarism Detectors via Subsequence Merging

Plagiarism is a significant challenge in computer science education. Thus, tool-based approaches are widely used to combat software plagiarism. However, especially due to the recent rise of automated obfuscation via algorithmic or AI-based techniques, these tools face difficulties due to increasingly sophisticated obfuscation techniques. To address this challenge, we present a novel defense mechanism against automated obfuscation attacks. This mechanism iteratively merges matching program subsequences to counteract the effects of the obfuscation. Our approach is language-independent, attack-agnostic, and integrates well into state-of-the-art software plagiarism detectors. The evaluation based on five real-world datasets indicates that our approach not only provides broader resilience against algorithmic and AI-based obfuscation attacks than the state-of-the-art but also improves the detection of fully AI-generated programs.