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Multi-medical image protection: compression–encryption scheme based on TLNN and mask cubes

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Abstract

With the massive use of online medical image data, the protection of the private information of patients has become an important issue to be addressed. A tabu learning neuron network (TLNN) and mask cubes produced from the original images based medical multi-image compression–encryption (MICE) scheme are designed to solve this issue. Firstly, multiple 2D images are compressed by using compression sensing (CS) technology, then the compressed images are integrated into eight small cubes, considering them as 3D cubes. Next, chaotic sequences iterated by the TLNN are used to integrate the small cubes into one large cube, which is subjected to confusion operations by using the 3D shuffling algorithm. And then, two complementary mask cubes are generated with the large cube, and they are used to perform different diffusion operations on the large cube to obtain two new image cubes. Finally, the two image cubes are combined to complete the MICE scheme. The performance test results nicely prove the compatibility, efficiency and safety of the scheme, and guarantee the security of medical images.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant no. 62061014); Technological innovation projects in the field of artificial intelligence in Liaoning province (Grant nos. 2023JH26/10300011); Basic scientific research projects in department of education of Liaoning Province (Grant no. LJ212410152049).

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Authors and Affiliations

  1. School of Information Science and Engineering, Dalian polytechnic University, Dalian, 116034, China

    Linlin Tan, Yinghong Cao & Jun Mou

  2. Department of Mathematical Sciences, Giuseppe Luigi Lagrange, Politecnico di Torino, Turin, 10129, Italy

    Santo Banerjee

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  1. Linlin Tan
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  2. Yinghong Cao
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Linlin Tan designed and carried out experiments, data analysis and manuscript editing. Jun Mou, Yinghong Cao and Santo Banerjee made the theoretical guidance for this paper. All authors reviewed the manuscript.

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Correspondence to Jun Mou.

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Tan, L., Cao, Y., Banerjee, S. et al. Multi-medical image protection: compression–encryption scheme based on TLNN and mask cubes. J Supercomput 81, 96 (2025). https://doi.org/10.1007/s11227-024-06624-6

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