基于汉明码的图像信息隐藏技术研究

发布时间：2018-07-03 21:10

本文选题：信息隐藏 + 汉明码　；参考：《安徽大学》2017年硕士论文

【摘要】：随着信息科技技术和多媒体技术的飞速发展,信息技术已融入到人们生活的每一个角落,并逐渐改变人们的生产生活方式。互联网为人们带来便利的同时,也面临着严峻的网络安全问题。因此,如何保护互联网中有价值的数据在存储或传递过程中的安全,保护所有终端受众的隐私安全,防止非法的第三者中途拦截或篡改,已成为信息安全领域研究的热点。信息隐藏技术作为网络安全技术的重要分支,为秘密信息在互联网上存储和传输的安全性提供了有效的保障。为了更好的提升数据在传输和存储中的安全性,本文深入研究了数字图像信息隐藏算法、汉明码技术以及其他图像处理技术,并提出了两个基于汉明码的信息隐藏方法,分别是明文域的信息隐藏方法和密文域的信息隐藏方法。在明文域的信息隐藏方法中,通过对汉明码技术的深入研究,发现多个汉明码结合应用在图像的信息隐藏上,会对伪装图像的质量有所提升。此外,还利用人眼对边缘区域较不敏感,而对平滑区域较为敏感的事实,结合图像处理中常用的边缘检测技术,提出了一种自适应的信息隐藏算法。该方法中,首先对图像划分相同大小的区块,并根据边缘检测结果给出一个区块陡峭等级的划分算法。实验结果显示,该算法具有较高的信息负载量,且有较好的不可感知性。为了便于管理且减轻发送者的负担,信息嵌入操作以及与接收者的交互操作均可由半可信的第三方服务器承担。出于保护图像拥有者的数据隐私的目的,需要对原始图像进行加密,第三方服务器则需要在密文图像上进行信息隐藏。由于加密技术会使相邻像素之间的相关性消失,这无疑对密文图像上的信息隐藏技术是一个巨大的挑战。值得庆幸的是,基于汉明码进行信息隐藏的方法,无需借助相邻像素间具有相关性这一特性。在密文域的信息隐藏方法中,通过研究汉明码技术,提出并证明了基于(7,4)汉明码的完美串定理,并基于该定理本文提出了一种高藏量的密文图像上的信息隐藏算法。实验结果显示,该算法所用的图像加密算法非常安全,大幅提高了信息负载量的同时,能够保证提取的秘密信息准确无误。
[Abstract]:With the rapid development of information technology and multimedia technology, information technology has been integrated into every corner of people's lives, and gradually changed people's way of production and life. The Internet brings convenience to people, at the same time, it also faces the severe network security problem. Therefore, how to protect the security of valuable data in the Internet, to protect the privacy of all terminal audiences, and to prevent illegal third parties from intercepting or tampering in the middle of the way, has become a hot topic in the field of information security. As an important branch of network security technology, information hiding technology provides an effective guarantee for the security of secret information storage and transmission on the Internet. In order to improve the security of data transmission and storage, this paper deeply studies the digital image information hiding algorithm, hamming code technology and other image processing technologies, and proposes two information hiding methods based on hamming code. The information hiding methods of plaintext domain and ciphertext domain are respectively. In the information hiding method of plaintext domain, through the in-depth study of hamming code technology, it is found that the quality of camouflaged image can be improved by combining multiple hamming codes with image information hiding. In addition, an adaptive information hiding algorithm is proposed based on the fact that the human eye is less sensitive to the edge region and more sensitive to the smooth region, combined with the commonly used edge detection techniques in image processing. In this method, the image is first divided into blocks of the same size, and an algorithm of dividing blocks with steep grades is presented according to the results of edge detection. The experimental results show that the algorithm has higher information load and better imperceptibility. In order to facilitate management and reduce the burden on the sender, the information embedding operation and the interaction operation with the receiver can be undertaken by the semi-trusted third party server. For the purpose of protecting the data privacy of the image owner, the original image needs to be encrypted, while the third party server needs to hide the information on the ciphertext image. Because encryption technology will make the correlation between adjacent pixels disappear, this is undoubtedly a huge challenge to the information hiding technology on ciphertext images. Fortunately, the method of information hiding based on hamming code does not need to rely on the correlation between adjacent pixels. In the information hiding method of ciphertext domain, by studying the hamming code technology, the perfect string theorem based on (7) hamming code is proposed and proved. Based on this theorem, an information hiding algorithm on ciphertext image with high volume is proposed. The experimental results show that the image encryption algorithm used in this algorithm is very secure and can greatly increase the information load and ensure the accuracy of the extracted secret information.
【学位授予单位】：安徽大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP309.7;TP391.41

【参考文献】