一句话：提出基于代码属性图与图变换的可扩展框架，抵御重构混淆，显著提升抄袭代码检测。
教育场景中的代码抄袭检测正受到日益复杂的混淆技术挑战，尤其是自动化的重构攻击。现有教育实践中的检测系统虽能应对基本混淆，却难以识别保持程序语义的结构修改，特别是重构混淆。本文提出一种新颖且可扩展的框架，通过代码属性图（CPG）和图变换增强当前最先进的检测器，以抵消基于重构的混淆。作者对真实学生提交进行综合评估，并施加基于算法和基于AI的混淆攻击；结果表明，该框架能显著提高抄袭代码的检测效果。
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.
@inproceedings{abs-2510-25057,
title = {Same Same But Different: Preventing Refactoring Attacks on Software Plagiarism Detection},
author = {Robin Maisch and Larissa Schmid and Timur Sağlam and Nils Niehues},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3773225}
}