Xu Xitong, Chen Shengbo
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China.
Entropy (Basel). 2022 Apr 7;24(4):521. doi: 10.3390/e24040521.
In this paper, aiming to solve the problem of vital information security as well as neural network application in optical encryption system, we propose an optical image encryption method by using the Hopfield neural network. The algorithm uses a fuzzy single neuronal dynamic system and a chaotic Hopfield neural network for chaotic sequence generation and then obtains chaotic random phase masks. Initially, the original images are decomposed into sub-signals through wavelet packet transform, and the sub-signals are divided into two layers by adaptive classification after scrambling. The double random-phase encoding in 4 system and Fresnel domain is implemented on two layers, respectively. The sub-signals are performed with different conversions according to their standard deviation to assure that the local information's security is guaranteed. Meanwhile, the parameters such as wavelength and diffraction distance are considered as additional keys, which can enhance the overall security. Then, inverse wavelet packet transform is applied to reconstruct the image, and a second scrambling is implemented. In order to handle and manage the parameters used in the scheme, the public key cryptosystem is applied. Finally, experiments and security analysis are presented to demonstrate the feasibility and robustness of the proposed scheme.
在本文中，为了解决光学加密系统中的重要信息安全问题以及神经网络应用问题，我们提出了一种利用霍普菲尔德神经网络的光学图像加密方法。该算法使用模糊单神经元动态系统和混沌霍普菲尔德神经网络来生成混沌序列，进而获得混沌随机相位掩模。首先，通过小波包变换将原始图像分解为子信号，子信号在置乱后通过自适应分类分为两层。分别在这两层上实现4f系统和菲涅耳域中的双随机相位编码。子信号根据其标准差进行不同的变换，以确保局部信息的安全性。同时，将波长和衍射距离等参数作为附加密钥，可增强整体安全性。然后，应用小波包逆变换重建图像，并进行第二次置乱。为了处理和管理该方案中使用的参数，应用了公钥密码系统。最后，通过实验和安全性分析来证明所提方案的可行性和鲁棒性。
