Dynamical analysis of high-order Hopfield neural network with application in WBANs

Abstract

Given the increasing demand for privacy preservation of medical data, a novel medical image encryption scheme is proposed based on Invertible Neural Network (INN) in this paper. Firstly, a new High-Order Hopfield Neural Network (HOHNN) is designed to yield a cipher flow as the conditional input, thereby regulating the encryption process. Moreover, its intrinsic properties as well as various dynamic behaviors are both demonstrated through theoretical analysis and two-parameter Lyapunov exponential charts. Secondly, guided by a multi-objective loss function and a known prior distribution, the original medical image is encrypted into a noise-like cipher image through the proposed encryption scheme. Besides, it is worth mentioning that unlike the existing deep learning-based data encryption schemes, the decryption network shares the identical weight matrix and network structure with the corresponding encryption one in this work. Finally, extensive simulation experiments have validated the feasibility as well as the security of the proposed scheme.