Abstract
In this paper, a multi-image encryption scheme based on compressed sensing (CS) and deep learning in the optical gyrator domain is proposed. Firstly, multiple plaintext images are compressed by CS to obtain multiple measurements, and then the pixels of each measurement are scrambled by using a chaotic system. Secondly, the scrambled measurements are combined into a matrix and diffused by XOR operation with a chaotic matrix. Finally, the diffused matrix is encoded with a random phase and an optical gyrator transform to obtain a complex-valued matrix, and the amplitude of the complex-valued matrix is taken as the ciphertext. In decrypt, plaintext images are reconstructed from the CS measurements by a neural network, which achieves high reconstruction speed and quality compared with the traditional algorithm. Especially, the data amount of plaintext images can be compressed by up to 8 times while achieving high decryption quality. To our best knowledge, CS reconstruction algorithms based on deep learning is firstly used for image encryption. Moreover, the proposed scheme is highly robust against occlusion, noise, and chosen-plaintext attack.
Highlights
With the rapid development of internet technology and the frequent occurrence of communication privacy and information leakage, information security has become increasingly emphasized
Inspired by the above analysis, a multi-image encryption scheme based on compressed sensing (CS) and deep learning in the optical gyrator domain is proposed in this paper
This scheme can compress and encrypt multiple images simultaneously, and the data amount of plaintext images can be compressed by up to 8 times while achieving high decryption quality. It is highly robust against noise and occlusion, and resistant to the reported chosen-plaintext attacks based on deep learning
Summary
With the rapid development of internet technology and the frequent occurrence of communication privacy and information leakage, information security has become increasingly emphasized. Inspired by the above analysis, a multi-image encryption scheme based on CS and deep learning in the optical gyrator domain is proposed in this paper In this scheme, CS compression is firstly applied to multiple plaintext images to obtain multiple measurements, and the pixels of each measurement are scrambled with a logistic map chaotic system. A proposed neural network is used to reconstruct multiple plaintext images from CS measurements during decryption, which has better reconstruction quality and faster reconstruction speed compared with traditional algorithms This scheme can compress and encrypt multiple images simultaneously, and the data amount of plaintext images can be compressed by up to 8 times while achieving high decryption quality. Numerical simulation results verify the effectiveness and feasibility of the proposed encryption scheme
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