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Avalanche effect of image encryption based on the Cycle-GAN

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Abstract

Aiming at the problem of the weak avalanche effect in the recently proposed deep learning image encryption algorithm, this paper analyzes the causes of weak avalanche effect in the neural network of Cycle-GAN step-by-step process and proposes an image encryption algorithm combining the traditional diffusion algorithm and deep learning neural network. In this paper, first, the neural network is used for image scrambling and slight diffusion, and then the traditional diffusion algorithm is used to further diffuse the pixels. The experiment in satellite images shows that our algorithm, with the help of the further diffusion mechanism, can compensate for the weak avalanche effect of Cycle-GAN-based image encryption and can change a pixel value to the original image, and the number of pixel change rate (NPCR) and unified average changing intensity (UACI) values can achieve 99.64% and 33.49%, respectively. In addition, our method can effectively encrypt the image where the encrypted image with high information entropy and low pixel correlation is obtained. The experiment on data loss and noise attack declares our method can identify the types and intensity of attacks. What is more, the key space is big enough, and the key sensitivity is high while the key has a certain randomness.

© 2021 Optical Society of America

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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