Novel discrete initial-boosted Tabu learning neuron: dynamical analysis, DSP implementation, and batch medical image encryption

A new discrete initial-boosted Tabu learning single neuron (DITLSN) is constructed using a new activation function to combine the Tabu learning neuron model in this paper. The complex and abundant dynamical behavior of the DITLSN is analyzed using bifurcation diagrams (BD) and Lyapunov exponential spectra(LEs), such as period, chaos, hyper-chaos, and attractor coexistence. In particular, the coexistence of initial-boosted hyperchaotic attractors found in numerical simulations of the DITLSN suggests that different initial states produce multiple highly complex attractors whose positions can change. The existence of attractor coexistence phenomenon makes chaotic parameters more difficult to be attacked by existing parameter identification algorithms, while its own high sensitivity to the initial state is more suitable for application scenarios such as secure communication. The results of the circuit implementation in the DSP platform are also in excellent agreement with the software simulation results. Finally, a batch medical image encryption scheme (BEIES) suitable for large-scale medical image encryption is designed. The performance analysis demonstrates that the designed scheme excels in the aspects of key performance, anti-statistical attack, anti-differential attack, robustness, etc., and most importantly, it has very high encryption and decryption efficiency, which is suitable for the application scenario of large-scale medical image batch processing.