基于深度学习的新混沌信号及其在图像加密中的应用

A new chaotic signal based on deep learning and its application in image encryption

为提高单一混沌系统图像加密的安全性,本文提出了基于深度学习的图像加密算法.首先,利用超混沌Lorenz系统产生混沌序列.其次,利用长短期记忆人工神经网络(long-short term memory,LSTM)复杂的网络结构模拟混沌特征构造新的混沌信号.然后,利用最大Lyapunov指数,0-1测试,功率谱分析、相图以及NIST测试对新信号的动力学特征进行分析.最后,将新信号应用到图像加密中.由于该方法生成的新信号不同于原有混沌信号,而且加密系统具有很高的复杂结构和非线性特征,故很难被攻击者攻击.仿真实验结果表明,本文提出的图像加密算法相比其他一些传统方法具有更高的安全性,能够抵抗常见的攻击方式.

To improve the security of image encryption in singular chaotic systems,an encryption algorithm based on deep-learning is proposed in this paper.To begin with,the chaos sequence is generated by using a hyperchaotic Lorenz system,prior to creating new chaotic signals based on chaotic characteristics obtained from he simulations of the powerful complex network structure of long-short term memory artificial neural network(LSTM-ANN).Then,dynamic characteristics of the new signals are analyzed with the largest Lyapunov exponent,0-1 test,power spectral analysis,phase diagrams and NIST test.In the end,the new signals are applied to image encryption,the results of which verify the expected increased difficulty in attacking the encrypted system.This is attributable to the differences of the new signals generated using the proposed method from the original chaotic signals,as well as arises from the high complexity and nonlinearity of the system.Considering its ability to withstand common encryption attacks,it is hence reasonable to conclude that the proposed method exhibits higher safety and security than other traditional methods.