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一种基于混沌系统的医学彩色图像加密新算法。

A New Algorithm for Medical Color Images Encryption Using Chaotic Systems.

作者信息

Moafimadani Seyed Shahabeddin, Chen Yucheng, Tang Chunming

机构信息

School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China.

出版信息

Entropy (Basel). 2019 Jun 10;21(6):577. doi: 10.3390/e21060577.

Abstract

In this paper, we present a new algorithm based on chaotic systems to protect medical images against attacks. The proposed algorithm has two main parts: A high-speed permutation process and adaptive diffusion. After the implementation of the algorithm in the MATLAB software, it is observed that the algorithm is effective and appropriate. Also, to quantitatively evaluate the uniformity of the histogram, the chi-square test is done. Key sensitivity analysis demonstrates that images cannot be decrypted whenever a small change happens in the key, which indicates that the algorithm is suitable. Clearly, part of special images is selected to test the selected plain-text, like an all-white image and an all-black image. Entropy results obtained from the implementation of the algorithm on this type of images show that the proposed method is suitable for this particular type of images. In addition, the obtained results from noise and occlusion attacks analysis show that the proposed algorithm can withstand against these types of attacks. Moreover, it can be seen that the images after encryption and decryption are of good quality; the measures such as the correlation coefficients, the entropy, the number of pixel change rate (NPCR), and the uniform average change intensity (UACI) have suitable values; and the method is better than previous methods.

摘要

在本文中,我们提出了一种基于混沌系统的新算法,用于保护医学图像免受攻击。所提出的算法有两个主要部分:高速置换过程和自适应扩散。在MATLAB软件中实现该算法后,观察到该算法是有效且合适的。此外,为了定量评估直方图的均匀性,进行了卡方检验。密钥敏感性分析表明,只要密钥发生微小变化,图像就无法解密,这表明该算法是合适的。显然,选择了部分特殊图像来测试所选的明文,如全白图像和全黑图像。在这类图像上实施该算法所获得的熵结果表明,所提出的方法适用于这种特定类型的图像。此外,噪声和遮挡攻击分析的结果表明,所提出的算法能够抵御这些类型的攻击。而且,可以看出加密和解密后的图像质量良好;相关系数、熵、像素变化率(NPCR)和均匀平均变化强度(UACI)等指标具有合适的值;并且该方法优于先前的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a9e/7515066/5d66e05c2826/entropy-21-00577-g001.jpg

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