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Manish Kumar¹, Aneesh Sreevallabh Chivukula², Gunjan Barua²

¹Department of Mathematics, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Hyderabad, 500078, Telangana, India. manishkumar@hyderabad.bits-pilani.ac.in.

Scientific reports

|January 8, 2025

概括

本研究介绍了一种使用深度卷积生成对抗网络 (DCGAN) 和虚拟行星域 (VPD) 的新型医疗图像加密方法. 该技术增强了对未经授权的访问和操纵的安全性,确保了患者的保密性.

科学领域:

医疗成像医学成像
网络安全网络安全
密码学密码学密码学密码学

背景情况:

医疗图像安全性对于患者保密性和数据完整性至关重要.
现有的加密方法可能无法充分解决复杂的威胁,如改和对抗性攻击.

研究的目的:

介绍一项用于医学图像的新型加密技术.
加强敏感医疗数据的安全性和保密性.

主要方法:

集成深度卷积生成对抗网络 (DCGAN) 和虚拟行星域 (VPD) 用于图像加密.
使用深度学习 (DL) 框架,时间,nonce和1D指数式切比舍夫图 (1-DEC) 生成加密密钥.

主要成果:

实验结果证明了该技术对未经授权的访问,改和对抗性攻击的有效性.
国家标准与技术研究所 (NIST) SP 800-22测试证实了密钥和加密图像的随机性.
该算法显示了对关键灵敏度,噪音,裁剪和对抗性攻击的稳定性,具有高效的时间和关键空间复杂性.

结论:

拟议的加密算法为医疗图像提供了高安全性和可靠性,如信息,相关系数,MSE,PSNR,NPCR和UACI指标所证明的那样.
统计分析和与现有算法的比较表明,拟议的方法适合在医疗环境中实际实施.

关键词:

混乱的地图混乱的地图在DCGAN中使用DCGAN.解密解密解密解密加密加密的加密方法在 VPD VPD 里面.

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