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Image encryption with quantum cellular neural network

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Abstract

A novel quantum image encryption scheme is proposed based on quantum cellular neural network with quantum operations and hyper-chaotic system, aiming to optimize security, computation complexity and decrypted image definition. The quantum operations, including quantum affine transforms, quantum SWAP and quantum CNOT gates, are controlled by hyper-chaotic signals to scramble image pixel coordinates and values, respectively. From our experiments, this scheme effectively reduces the computation complexity to O(n), and can completely recover the correct decrypted images under the premise of ensuring algorithm security. In addition, each stage of processing images can be implemented with quantum circuits, and the design circuits are experimentally examined for initial quantum image preparation. This means that the proposed scheme could be potentially implemented on quantum devices.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61972418, 61872390, 61801522, U1736113), the Natural Science Foundation of Hunan Province (Grant Nos. 2020JJ4750, 2019JJ40352), the Special Foundation for Distinguished Young Scientists of Changsha (Grant Nos. kq1905058) and CCF-Baidu Open Fund (NO.2021031).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Jinjing Shi, Shuhui Chen, Tian Chen, Tongge Zhao & Heyuan Shi

  2. Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA

    Jiuqi Tang

  3. School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan, 411301, China

    Qin Li

  4. China Greatwall Quantum Laboratory, China Greatwall Technology Group CO., LTD., Changsha, 410073, China

    Chunlin Yu

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  1. Jinjing Shi
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  2. Shuhui Chen
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  3. Tian Chen
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Shi, J., Chen, S., Chen, T. et al. Image encryption with quantum cellular neural network. Quantum Inf Process 21, 214 (2022). https://doi.org/10.1007/s11128-022-03555-0

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