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Quantum image encryption algorithm based on Arnold scrambling and wavelet transforms

Abstract

Based on the modified flexible representation of quantum images, a novel quantum image encryption algorithm was proposed in this paper. The encryption process performs Arnold scrambling operation to disturb the quantum image information in spatial domain first. Then, quantum wavelet transforms are employed to decompose the scrambled quantum image into multiscale resolution (i.e., a sequence of subimages) in the frequency domain, which are mainly divided into two parts: the low-frequency component (i.e., the approximation) and high-frequency detail information (i.e., the horizontal details, vertical details and diagonal details in each decomposition level). Following that, Arnold scrambling operations are implemented to encrypt the wavelet coefficients within each subimage in the frequency domain once again. Finally, based on inverse quantum wavelet transforms, the encrypted wavelet coefficients can affect the pixel values of the entire reconstructed quantum images. Due to the fact that all the quantum operations are invertible, the decryption process of the encrypted image is performed in a straightforward manner by reversing all of the quantum operations within quantum image encryption process. The proposed encryption algorithm is simulated on a classical computer with MATLAB environments. Experimental results and numerical analysis indicate that the presented algorithm has a good encrypted effect and high security.

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Acknowledgements

This work is supported by the National Key R&D Plan under Grant Nos. 2018YFC1200200 and 2018YFC1200205 and Scientific Research Fund of Hunan Provincial Education Department under Grant No. 18B420.

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Correspondence to Ri-Gui Zhou.

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Hu, W., Zhou, R., Luo, J. et al. Quantum image encryption algorithm based on Arnold scrambling and wavelet transforms. Quantum Inf Process 19, 82 (2020) doi:10.1007/s11128-020-2579-9

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Keywords

  • Quantum computing
  • Image encryption
  • Arnold scrambling
  • Discrete wavelet transforms
  • Computational complexity