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创新的QR码系统用于防改生成和防欺诈验证
1Department of Computer Science, College of Computer, Qassim University, Buridah 51452, Saudi Arabia.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究介绍了一种安全的快速响应 (QR) 代码系统,使用数字水印和神经网络来防止条形码欺诈. 这种创新方法有效地识别欺诈性的QR码,增强自动识别安全性.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 数据完整性 数据完整性
背景情况:
- 条码技术广泛用于自动数据捕获,面临着重大安全漏洞,特别是条码替代欺诈.
- 现有的条形码系统缺乏强大的机制来防止改和确保数据真实性.
- 越来越多的人依赖自动识别系统,需要先进的安全解决方案.
研究的目的:
- 开发和评估一个创新的系统,用于安全的快速响应 (QR) 代码生成和验证.
- 加强QR码的完整性,防止未经授权的修改和欺诈.
- 引入基于神经网络的认证模型来验证QR码的合法性.
主要方法:
- 实施数字水印技术,在QR码中嵌入防改信息.
- 开发基于神经网络的身份验证模型,用于QR码验证.
- 使用5000个QR码样本的数据集进行实验评估.
主要成果:
- 拟议的系统在区分真实和欺诈性的QR码方面表现出高度准确性.
- 数字水印成功增强了QR码的完整性,使它们更耐改.
- 神经网络模型在验证扫描QR码时被证明是有效的.
结论:
- 开发的系统提供了一种有效的解决方案,用于防止现实应用中的QR码欺诈.
- 数字水印和基于神经网络的身份验证显著提高了自动识别系统的安全性.
- 这项研究有助于提高基于条形码的数据捕获流程的可靠性.
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