A novel neural network approach for digital image data encryption/decryption

S. Joshi, V. Udupi, D. R. Joshi
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引用次数: 15

Abstract

With the increased popularity of multimedia applications, there is a great demand for secured data storage and transmission techniques. Information security has traditionally been ensured with data encryption and authentication techniques. Through the years, different generic data encryption standards have been developed. The secrecy of communication is maintained by secret key exchange. In effect the strength of the algorithm depends solely on the length of the key. The presented work aims at secure image transmission using randomness in encryption algorithm, thereby creating more confusion to obtain the original data. The security of the original cipher has been enhanced by addition of impurities to misguide the cryptanalyst. Since the encryption process is one way function, the artificial neural networks are best suited for this purpose as they possess features like high security, no distortion and its ability to perform for non linear input-output characteristics, In the presented work the need for key exchange is also eliminated, which is otherwise a perquisite for most of the algorithms used today. The proposed work finds its application in medical imaging systems, military image database communication and confidential video conferencing, and similar such application. The results are obtained through the use of MATLAB 7.0.1.
一种新的数字图像数据加解密神经网络方法
随着多媒体应用的日益普及,对安全的数据存储和传输技术提出了更高的要求。传统上,信息安全是通过数据加密和身份验证技术来确保的。多年来,已经开发了不同的通用数据加密标准。通信的保密性是通过密钥交换来保持的。实际上,算法的强度仅取决于密钥的长度。本文的工作旨在利用加密算法中的随机性来实现图像的安全传输,从而给原始数据的获取带来更多的混乱。通过添加杂质来误导密码分析者,原始密码的安全性得到了增强。由于加密过程是单向函数,人工神经网络最适合这一目的,因为它们具有高安全性,无失真及其执行非线性输入输出特性的能力。在本文中,还消除了密钥交换的需要,这是目前使用的大多数算法的先决条件。该工作可应用于医学成像系统、军事图像数据库通信和机密视频会议等领域。结果是通过MATLAB 7.0.1得到的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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