Detecting Automatic Software Plagiarism via Token Sequence Normalization

While software plagiarism detectors have been used for decades, the assumption that evading detection requires programming proficiency is challenged by the emergence of automated pla-giarism generators. These generators enable effortless obfus-cation attacks, exploiting vulnerabilities in existing detectors by inserting statements to disrupt the matching of related programs. Thus, we present a novel, language-independent de-fense mechanism that leverages program dependence graphs, rendering such attacks infeasible. We evaluate our approach with multiple real-world datasets and show that it defeats plagiarism generators by offering resilience against automated obfuscation while maintaining a low rate of false positives.