Dual Synchronization of Fractional-Order Complex-Valued Neural Networks with Application to Medical Image Encryption - Zerimeche, Hadjer; Houmor, Tarek (Journal of Applied Nonlinear Dynamics, 2026)
This paper explores a novel dual synchronization method for fractional-order complex-valued neural networks (FOCVNN) by combining adaptive control, inequality techniques, and stability theory of fractional calculus. The FOCVNN are separated into two real-valued parts and two imaginary-valued parts. According to the proposed dual synchronization scheme, a chaotic masking method is suggested for encrypting medical images to preserve patient information. Simulation results demonstrate the efficiency of the control laws and parameter updating equations. Additionally, experimental results including histograms, change rate of the number of pixels in the cipher-image (NPCR), unified average changing intensity (UACI), peak signal-to-noise ratio (PSNR) and correlation confirm the effectiveness of the proposed method for secure image communication.