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

ArXi:2603.27734v1 Announce Type: cross 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.