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Multi-Image Encryption Based on Compressed Sensing and Deep Learning in Optical Gyrator Domain

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 chaot...

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Published in:IEEE photonics journal 2021-06, Vol.13 (3), p.1-16
Main Authors: Ni, Renjie, Wang, Fan, Wang, Jun, Hu, Yuhen
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description 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.
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source IEEE Xplore Open Access Journals
subjects Algorithms
Chaos theory
compressed sensing
Deep learning
Domains
Encryption
Gyrators
Image coding
Image reconstruction
Machine learning
Multi-image encryption
Neural networks
Occlusion
Optical diffraction
optical gyrator transform
Optical imaging
Optical sensors
title Multi-Image Encryption Based on Compressed Sensing and Deep Learning in Optical Gyrator Domain
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