一句话：提出基于对比学习的智能合约漏洞检测方法，利用合约间相关性提升检测性能。
智能合约漏洞已成为威胁区块链交易安全的重要因素。现有最先进的深度学习方法通常将每份输入合约视为独立实体，通过拟合漏洞标签来学习漏洞模式，却忽略了合约之间的相关性：既未利用同类合约之间的共性，也未区分不同类型合约之间的差异，导致检测性能不理想。针对这一问题，本文提出基于对比学习的智能合约漏洞检测方法，旨在通过建模合约之间的关系来弥补上述缺陷，从而改进智能合约安全性。摘要未报告具体方法与评估结果。
Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerability labels. It is a pity that they disregard the correlation between contracts, failing to consider the commonalities between contracts of the same type and the differences among contracts of different types. As a result, the performance of these methods falls short of the desired level.