安全鲁棒的光学多级医学图像密码系统

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
W. El-shafai, M. Aly, A. Algarni, F. El-Samie, N. Soliman
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引用次数: 9

摘要

:由于远程医疗和医疗保健服务的快速增长,彩色医学图像安防的应用范围急剧扩大。提出了一种基于相位截断分数阶傅里叶变换的非对称彩色医学图像密码系统。分数傅里叶平面和输出平面中的两个不同相位作为解密密钥。因此,加密密钥将不会用于解密过程。因此,与传统的光学对称OSH(光学扫描全息)和DRPE(双随机相位编码)算法相比,所引入的PTFrFT算法包括非对称的加密和解密过程。引入的非对称密码系统的主要影响之一是,由于其相位非线性截断分量的显著特征,它消除了相关对称密码系统的一维性。通过对各种彩色医学图像的比较,验证了该密码系统的有效性。实验结果表明,所引入的密码系统具有鲁棒性和安全性。与传统的加密算法相比,即使在存在噪声和严重信道攻击的情况下,它也具有出色的加密性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Secure and Robust Optical Multi-Stage Medical Image Cryptosystem
: Due to the rapid growth of telemedicine and healthcare services, color medical image security applications have been expanded precipitously. In this paper, an asymmetric PTFrFT (Phase Truncated Fractional Fourier Transform)-based color medical image cryptosystem is suggested. Two different phases in the fractional Fourier and output planes are provided as deciphering keys. Accordingly, the ciphering keys will not be employed for the deciphering procedure. Thus, the introduced PTFrFT algorithm comprises asymmetric ciphering and deciphering processes in contrast to the traditional optical symmetric OSH (Optical Scanning Holography) and DRPE (Double Random Phase Encoding) algorithms. One of the principal impacts of the introduced asymmetric cryptosystem is that it eliminates the one-dimensionality aspects of the related symmetric cryptosystems due to its remarkable feature of phase nonlinear truncation components. More comparisons on various color medical images are examined and analyzed to substanti-ate the cryptosystem efficacy. The achieved experimental outcomes ensure that the introduced cryptosystem is robust and secure. It has terrific cryptography performance compared to conventional cryptography algorithms, even in the presence of noise and severe channel attacks.
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来源期刊
Cmc-computers Materials & Continua
Cmc-computers Materials & Continua 工程技术-材料科学:综合
CiteScore
5.30
自引率
19.40%
发文量
345
审稿时长
1 months
期刊介绍: This journal publishes original research papers in the areas of computer networks, artificial intelligence, big data management, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, data analysis, modeling, and engineering of designing and manufacturing of modern functional and multifunctional materials. Novel high performance computing methods, big data analysis, and artificial intelligence that advance material technologies are especially welcome.
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