A joint image encryption based on a memristive Rulkov neuron with controllable multistability and compressive sensing

Abstract

Image encryption, as a critical branch, has attracted increasing attention to the demand for information security specifically in the era of artificial intelligence (AI). A chaotic sequence is regarded as an important encryption source, and compressive sensing provides an effective technology for obtaining and reconstructing sparse or compressible signals in applied electronic engineering. In this work, the detouring matching pursuit algorithm and DNA coding are utilized to increase the performance based on a newly developed chaotic firing neuron. A memristor as the electromagnetic component is proven to enhance synaptic plasticity and emulate the synaptic connections in the brain. A unique discrete memristive neuron is derived for exploring the dynamics of neuron firing. By modifying the feedback from the memristor various coexisting neuronal chaotic firing are possessed. Because of the periodic evolution of the resistor from the memristor, the derived memristive Rulkov neuron exhibits coexisting homogeneous and heterogeneous multistability, which enables amplitude controllability and different types of coexisting chaotic firings. Circuit implementation based on CH32 is built to verify the controllability of the coexisting dynamics.