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Medical image cipher using hierarchical diffusion and non-sequential encryption

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Abstract

This paper presents a medical image cipher with hierarchical diffusion and non-sequential encryption mechanism. An improved permutation approach is developed, which can contribute to not only image shuffling but also lightweight pixel modification at the same time. Therefore, diffusion property of the proposed scheme is achieved from both the permutation and diffusion phases, and hierarchical diffusion effect is thus obtained. Besides, non-sequential visiting mechanism is developed to encrypt the plain pixels in secret order; in other words, pixel visiting order is exploited as extra encryption factor in addition to the traditional pixel location and gray value. With hyper-chaotic Lorenz system employed as key stream generator, a complete image encryption scheme is finally constructed. Experimental results and security analyses validate the effectiveness and superiority of the proposed cipher.

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Notes

  1. Medical images with 256 gray levels are employed for demonstration, whereas the extension to higher resolution images are straightforward.

  2. The natural images are taken from the USC-SIPI Image Database (http://sipi.usc.edu/database/). The medical images are taken from a local hospital and have been de-identified, they are not presented in this paper for concise representation and interesting readers can find these images in [37] (Figure 3) which is another paper of our team.

  3. Some performance indicators of Lian’s work [6] are reported in [16] and those of Chen’s work [4] can be found in [58].

  4. The average item is mean of the absolute involved correlation coefficients.

  5. The ciphers in [9, 26] can get high NPCR in the first encryption round, yet satisfactory UACI can only be obtained in the second round.

  6. Wang’s cipher is an exception [13], as his variable control parameter definitely results in distinct key elements in different rounds; besides, the permutation is assumed to be iterated three times.

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Acknowledgements

This work is funded by the National Natural Science Foundation of China (No. 61802055), Fundamental Research Funds for the Central Universities (No. N171903003), Postdoctoral Science Foundation of Northeastern University (No. 20180101), China Postdoctoral Science Foundation (No. 2018M630301). Thanks to Dr Zhongyun Hua (Harbin Institute of Technology Shenzhen Graduate School) for his valuable suggestions. Thanks to Prof. Yicong Zhou (University of Macau) for his selfless suggestions on information security and technical writing, Junxin Chen has started his Postdoctoral Fellowship under the UM Macao Talent Program with Prof. Zhou’s supervision from 01/01/2019.

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Authors and Affiliations

  1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, 110169, China

    Junxin Chen

  2. School of Sciences, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Lei Chen

  3. School of Information Technology, Deakin University, Geelong, VIC, 3216, Australia

    Leo Yu Zhang

  4. Software College, Northeastern University, Shenyang, 110169, China

    Zhi-liang Zhu

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  1. Junxin Chen
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  2. Lei Chen
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  3. Leo Yu Zhang
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Correspondence to Junxin Chen.

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Chen, J., Chen, L., Zhang, L.Y. et al. Medical image cipher using hierarchical diffusion and non-sequential encryption. Nonlinear Dyn 96, 301–322 (2019). https://doi.org/10.1007/s11071-019-04791-3

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