Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection

Abstract Due to their biological interpretability, memristors are widely used to simulate synapses between artificial neural networks. As a type of neural network whose dynamic behavior can be explained, the coupling of resonant tunneling diode-based cellular neural networks (RTD-CNNs) with memristors has rarely been reported in the literature. Therefore, this paper designs a coupled RTD-CNN model with memristors (RTD-MCNN), investigating and analyzing the dynamic behavior of the RTD-MCNN. Based on this model, a simple encryption scheme for the protection of digital images in police forensic applications is proposed. The results show that the RTD-MCNN can have two positive Lyapunov exponents, and its output is influenced by the initial values, exhibiting multistability. Furthermore, a set of amplitudes in its output sequence is affected by the internal parameters of the memristor, leading to nonlinear variations. Undoubtedly, the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy protection. Encryption tests and security analyses validate the effectiveness of this scheme.