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Image encryption based on actual chaotic mapping using optical reservoir computing

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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.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported in part by the Innovation Research 2035 Pilot Plan of Southwest University and Fundamental Research Funds for the Central Universities under Grant SWU-XDPY22013; in part by the Chongqing Talent Plan under Grant cstc2022ycjh-bgzxm0165; in part by the Special funds for Postdoctoral research of Chongqing under Grant 2010010004713415; and in part by the Chongqing Normal University Ph.D. Startup Fund under Grant 21XLB035.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Xiao Jiang, Yiyuan Xie, Bocheng Liu, Junxiong Chai, Yichen Ye, Manying Feng & Haodong Yuan

  2. The Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing, Chongqing, 400715, China

    Yiyuan Xie

  3. School of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China

    Tingting Song

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  1. Xiao Jiang
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Correspondence to Yiyuan Xie.

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Jiang, X., Xie, Y., Liu, B. et al. Image encryption based on actual chaotic mapping using optical reservoir computing. Nonlinear Dyn 111, 15531–15555 (2023). https://doi.org/10.1007/s11071-023-08666-6

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