Vulnerability analysis of optical encryption schemes using deep learning (DL) has recently become of interest to many researchers. However, very few works have paid attention to the design of optical encryption systems using DL. Here we report on the combination of the holographic method and DL technique for optical encryption, wherein a secret image is encrypted into a synthetic phase computer-generated hologram (CGH) by using a hybrid non-iterative procedure. In order to increase the level of security, the use of the steganographic technique is considered in our proposed method. A cover image can be directly diffracted by the synthetic CGH and be observed visually. The speckle pattern diffracted by the CGH, which is decrypted from the synthetic CGH, is the only input to a pre-trained network model. We experimentally build and test the encryption system. A dense convolutional neural network (DenseNet) was trained to estimate the relationship between the secret images and noise-like diffraction patterns that were recorded optically. The results demonstrate that the network can quickly output the primary secret images with high visual quality as expected, which is impossible to achieve with traditional decryption algorithms.

中文翻译：

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使用深度学习的全息和散斑加密

使用深度学习 (DL) 的光学加密方案的漏洞分析最近引起了许多研究人员的兴趣。然而，很少有工作关注使用DL设计光学加密系统。在这里，我们报告了用于光学加密的全息方法和 DL 技术的组合，其中通过使用混合非迭代程序将秘密图像加密为合成阶段的计算机生成全息图 (CGH)。为了提高安全级别，在我们提出的方法中考虑使用隐写技术。封面图像可以通过合成 CGH 直接衍射并进行视觉观察。从合成 CGH 解密的 CGH 衍射的散斑图案是预训练网络模型的唯一输入。我们实验性地构建和测试加密系统。训练密集的卷积神经网络 (DenseNet) 来估计秘密图像与光学记录的类噪声衍射图案之间的关系。结果表明，该网络可以按预期快速输出具有较高视觉质量的初级秘密图像，这是传统解密算法无法实现的。