Optical Image Encryption Using Fractional-Order Quantum Cellular Neural Networks in a Fractional Fourier Domain
Xinchen Liu, Xiaofeng Jin, Yahui Zhao
Based on a fractional-order quantum cellular neural network (QCNN), we present a new image encryption method. This technique uses an anamorphic fractional Fourier transform and was designed to solve the current insufficiency of nonlinearities that exist in optical cryptosystems. The primary advantage of an anamorphic transform is the additional degree of freedom offered from the fractional order, which increases flexibility of the optical system. First and second chaos random phase masks were obtained through the fractional-order QCNN system with varying parameters which provide a complex key space. An input image was processed by the first chaos random phase mask and then transformed using an anamorphic fractional Fourier transform. An encrypted image was then produced using the second chaos random phase mask and the second anamorphic fractional Fourier transform. The cryptosystem exhibited powerful resistance against many sorts of potential attacks, such like noise and occlusion. The simulation experiments verify the security and effectiveness of scheme in this paper.
International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)•Jul 1, 2018•8 citations