基于YOLOv12的在线考试作弊检测和区块链可信存款
Haoliang Wang1,2, Zarina Shukur3, Khairul Akram Zainol Ariffin4
1Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia. p109025@siswa.ukm.edu.my.
Scientific reports
|September 27, 2025
概括
这项研究引入了一种新的系统,用于在线考试作弊检测,使用轻量级的YOLOv12模型和区块链技术. 它增强了实时检测准确性,并确保了对学术诚信的防改证据的保护.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 在线考试提供了灵活性,但面临着来自多样化和秘密作弊行为的挑战.
- 目前的反作弊方法缺乏实时检测能力和可靠的证据管理.
- 维护在线评估的公平性和完整性至关重要.
研究的目的:
- 为实时在线考试作弊检测开发一个综合解决方案.
- 提高作弊检测模型的效率和准确性.
- 建立一个可信和不可变的系统,以保护作弊的证据.
主要方法:
- 对YOLOv12模型 (YOLOv12NoAttn) 进行轻量化修改,通过优化骨干和头部网络来实现更快的推断.
- 实施使用Hyperledger Fabric区块链和IPFS分布式存储的可信证据存储库.
- 在区块链上通过智能合约记录检测元数据和内容标识符 (CID),以确保证据的完整性.
主要成果:
- 轻量级的YOLOv12NoAttn模型实现了具有竞争力的检测性能,并提高了效率 (28%更少的参数,13%更少的GFLOPs).
- 实验结果显示,mAP50和RECALL的情况略有改善.
- 废弃性研究证实了模型轻量化优化的有效性.
结论:
- 拟议的综合系统为在线考试作弊提供了一个高效,准确和可靠的解决方案.
- 这种方法有助于维护在线评估环境中的公平性和诚信.
- 优化的人工智能模型和区块链技术的结合为学术诚信提供了强大的框架.
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