Xu Xitong, Chen Shengbo
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China.
Entropy (Basel). 2021 Apr 13;23(4):456. doi: 10.3390/e23040456.
Image encryption is a confidential strategy to keep the information in digital images from being leaked. Due to excellent chaotic dynamic behavior, self-feedbacked Hopfield networks have been used to design image ciphers. However, Self-feedbacked Hopfield networks have complex structures, large computational amount and fixed parameters; these properties limit the application of them. In this paper, a single neuronal dynamical system in self-feedbacked Hopfield network is unveiled. The discrete form of single neuronal dynamical system is derived from a self-feedbacked Hopfield network. Chaotic performance evaluation indicates that the system has good complexity, high sensitivity, and a large chaotic parameter range. The system is also incorporated into a framework to improve its chaotic performance. The result shows the system is well adapted to this type of framework, which means that there is a lot of room for improvement in the system. To investigate its applications in image encryption, an image encryption scheme is then designed. Simulation results and security analysis indicate that the proposed scheme is highly resistant to various attacks and competitive with some exiting schemes.
图像加密是一种防止数字图像中的信息泄露的保密策略。由于具有出色的混沌动态行为，自反馈霍普菲尔德网络已被用于设计图像密码。然而，自反馈霍普菲尔德网络结构复杂、计算量大且参数固定；这些特性限制了它们的应用。本文揭示了自反馈霍普菲尔德网络中的单个神经元动力学系统。单个神经元动力学系统的离散形式是从自反馈霍普菲尔德网络推导出来的。混沌性能评估表明，该系统具有良好的复杂性、高灵敏度和较大的混沌参数范围。该系统还被纳入一个框架以改善其混沌性能。结果表明该系统很好地适应了这种类型的框架，这意味着该系统有很大的改进空间。为了研究其在图像加密中的应用，随后设计了一种图像加密方案。仿真结果和安全性分析表明，所提出的方案对各种攻击具有高度抗性，并且与一些现有方案具有竞争力。
