[2603.27734] Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training

Computer Science > Machine Learning arXiv:2603.27734 (cs) [Submitted on 29 Mar 2026] Title:Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training Authors:Zeli Wang, Qingxuan Yang, Shuyin Xia, Yueming Wu, Bo Liu, Longlong Lin View a PDF of the paper titled Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training, by Zeli Wang and 5 other authors View PDF HTML (experimental) Abstract:Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning techniques. However, DNNs typically require large-scale labeled datasets to model the relationships between contract features and vulnerability labels. In practice, the labeling process often depends on existing open-sourced tools, whose accuracy cannot be guaranteed. Consequently, label noise poses a significant challenge for the accuracy and robustness of the smart contract, which is rarely explored in the literature. To this end, we propose Contrastive learning-enhanced Granular-Ball smart Contracts training, CGBC, to enhance the robustness of contract vulnerability detection. Specifically, CGBC first introduces a Granular-ball computing layer between the encoder layer and the classifier layer, to group similar contracts into Granular-Balls (GBs) and generate new coarse-grained representations (i.e., the center and the label ...