对于非对称计算机生成全息 (CGH) 加密系统的深度学习解密方法
Optics express
|November 14, 2024
概括
一个新的深度学习 (DL) 策略增强了对不对称的CGH加密系统的光学图像解密. ACGHC-Net实现了高保真解密,具有优异的抗噪声和裁剪强度,为无钥匙图像加密铺平了道路.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算机科学 计算机科学
- 密码学 密码学 密码学 密码学
背景情况:
- 无钥匙管理是光学图像解密的重要优势.
- 基于数字全息图 (DRPE) 和计算机生成全息图 (CGH) 的不对称加密系统正在获得引力.
- 阶段截断和混乱相口罩为安全的图像加密提供了独特的特性.
研究的目的:
- 为非对称的基于DRPE的CGH加密系统提出一个高保真度深度学习 (DL) 解密策略.
- 开发一种DL模型,能够准确高效地解密密码图像.
- 评估拟议的解密方法对噪声和图像裁剪的稳定性.
主要方法:
- 创建了一个加密文本和纯文本图像对的数据集.
- 一个深度神经网络,ACGHC-Net,是使用监督学习设计和训练的.
- 该网络结合了相切断和混乱的虹膜相口罩来解密.
主要成果:
- 该ACGHC-Net实现了高解密保真度,平均交叉相关系数 (CC) 为0.998.
- 图像质量优异,平均结构相似度 (SSIM) 为0.895,峰值信号噪声比 (PSNR) 为31.090dB.
- 该网络在加密复杂的灰度图像中表现出强大的抗噪声和抗切割强度.
结论:
- 拟议的ACGHC-Net提供了一个有效和强大的解决方案,用于在不对称的DRPE基础上的CGH加密系统中进行无钥匙解密.
- 基于DL的方法在解密速度和准确性方面提供了显著的改进.
- 预计这种方法将在光学图像加密系统中推进无钥匙解密技术.
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