一句话：提出 ReGraph 框架，高效跨架构与优化级别比较二进制函数，速度比 NLP 方法快 700 倍且精度相当。
二进制代码相似性检测对漏洞识别和代码复制检测至关重要，但因二进制剥离和多样编译环境而颇具挑战。现有方法多采用日益复杂的神经网络提升精度，计算时间随之增加，即使使用强大 GPU，大规模软件处理仍很耗时。为此，本文提出 ReGraph 框架，用于跨架构和优化级别高效比较二进制代码函数。作者在公共数据集上评估，结果显示 ReGraph 具有显著速度优势，比基于自然语言处理的方法快 700 倍，同时保持与当前最优模型相当的准确率。
Binary Code Similarity Detection (BCSD) is not only essential for security tasks such as vulnerability identification but also for code copying detection, yet it remains challenging due to binary stripping and diverse compilation environments. Existing methods tend to adopt increasingly complex neural networks for better accuracy performance. The computation time increases with the complexity. Even with powerful GPUs, the treatment of large-scale software becomes time-consuming. To address these issues, we present a framework called ReGraph to efficiently compare binary code functions across architectures and optimization levels. Our evaluation with public datasets highlights that ReGraph exhibits a significant speed advantage, performing 700 times faster than Natural Language Processing (NLP)-based methods while maintaining comparable accuracy results with respect to the state-of-the-art models.