Title
A Novel Image Encryption Algorithm Based On Least Squares Generative Adversarial Network Random Number Generator
Abstract
In cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This caused a conflict between the number of random keys and the speed of generation. In this paper, we introduce the Least Squares Generative Adversarial Networks(LSGAN)into random number generation. Using LSGAN's powerful learning ability, a novel learning random number generator is constructed. Six chaotic systems with different structures and different dimensions are used as training sets to realize the rapid and efficient generation of random numbers. Experimental results prove that the encryption key generated by this scheme can pass all randomness tests of the National Institute of Standards and Technology (NIST). Hence, our result shows that LSGAN has the potential to improve the quality of the random number generators. Finally, the results are successfully applied to the image encryption scheme based on selective scrambling and overlay diffusion, and good results are achieved.
Year
DOI
Venue
2021
10.1007/s11042-021-10979-w
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Random number generator, Least squares generative adversarial networks, NIST test, Chaotic system, Image encryption
Journal
80
Issue
ISSN
Citations 
18
1380-7501
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Zhenlong Man111.03
Jinqing Li235.81
Xiaoqiang Di397.31
Xu Liu400.34
Jian Zhou500.34
Jia Wang600.34
Xingxu Zhang700.68