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- Title
Image encryption based on actual chaotic mapping using optical reservoir computing.
- Authors
Jiang, Xiao; Xie, Yiyuan; Liu, Bocheng; Chai, Junxiong; Ye, Yichen; Song, Tingting; Feng, Manying; Yuan, Haodong
- Abstract
Driven by the growing significance of information security, the field of image encryption is attracting increasing interest. This work solves an image encryption issue of experiencing actual chaotic mapping via an optical neural network, extending our knowledge of image encryption based on chaos theory. In this paper, we first construct an optical neural network that is a four-channel signal injection optical reservoir computing (ORC) system relied on an optical dynamics system of the mutually coupled quantum dot spin-polarized vertical cavity surface emitting lasers (MCQD spin-VCSELs) subject to external optical injection, the dynamics of which is analyzed. Then, a secure and general image encryption scheme based on the ORC system is newly developed. The encryption process first requires the image to undergo an ORC-based actual chaotic mapping that achieves a transformation from the original image domain to the chaos domain. Then, one important role of the ORC system outputs is to create secret keys related to plain image combining with chaotic keys derived from the optical chaotic system, and the other one is to act as a transitional medium, being scrambled and diffused by the obtained plaintext-related keys, which further increase the security of our scheme, and finally obtaining the cipher-image. The simulation and analysis results demonstrate that the proposed image encryption scheme is effective and highly secure. Moreover, this study provides an important practical implication that brings the inspiration for future similar research into information security and will help other researchers design new encryption techniques based on chaos theory using neural networks.
- Subjects
IMAGE encryption; OPTICAL computing; SURFACE emitting lasers; IMAGING systems; INFORMATION technology security; CHAOS theory
- Publication
Nonlinear Dynamics, 2023, Vol 111, Issue 16, p15531
- ISSN
0924-090X
- Publication type
Article
- DOI
10.1007/s11071-023-08666-6