Multiple-Image Fusion Encryption (MIFE) Using Discrete Cosine Transformation (DCT) and Pseudo Random Number Generators

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Multimedia Information Retrieval Pub Date : 2020-06-30 DOI:10.5772/intechopen.92369
Lee Mariel Heucheun Yepdia, A. Tiedeu, Z. Lachiri
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引用次数: 2

Abstract

This chapter proposes a new multiple-image encryption algorithm based on spectral fusion of watermarked images and new chaotic generators. Logistic-May (LM), May-Gaussian (MG), and Gaussian-Gompertz (GG) were used as chaotic generators for their good properties in order to correct the flaws of 1D chaotic maps (Logistic, May, Gaussian, Gompertz) when used individually. Firstly, the discrete cosine transformation (DCT) and the low-pass filter of appropriate sizes are used to combine the target watermarked images in the spectral domain in two different multiplex images. Secondly, each of the two images is concatenated into blocks of small size, which are mixed by changing their position following the order generated by a chaotic sequence from the Logistic-May system (LM). Finally, the fusion of both scrambled images is achieved by a nonlinear mathematical expression based on Cramer’s rule to obtain two hybrid encrypted images. Then, after the decryption step, the hidden message can be retrieved from the watermarked image without any loss. The security analysis and experimental simulations confirmed that the proposed algorithm has a good encryption performance; it can encrypt a large number of images combined with text, of different types while maintaining a reduced Mean Square Error (MSE) after decryption.
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基于离散余弦变换和伪随机数发生器的多图像融合加密(MIFE)
本章提出了一种新的基于水印图像的频谱融合和新的混沌发生器的多图像加密算法。采用Logistic-May (LM)、May-Gaussian (MG)和Gaussian-Gompertz (GG)作为混沌发生器,以纠正1D混沌映射(Logistic、May、Gaussian、Gompertz)单独使用时的缺陷。首先,采用离散余弦变换(DCT)和适当大小的低通滤波器对两幅不同复用图像的谱域目标水印图像进行组合;其次,将两幅图像连接成小块,根据Logistic-May系统(LM)的混沌序列生成的顺序,通过改变它们的位置进行混合。最后,采用基于Cramer规则的非线性数学表达式对两张加密图像进行融合,得到两张混合加密图像。然后,经过解密步骤,可以在不丢失任何信息的情况下从水印图像中提取隐藏信息。安全性分析和实验仿真验证了该算法具有良好的加密性能;它可以对大量不同类型的图像和文本进行加密,同时在解密后保持较小的均方误差(MSE)。
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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来源期刊
CiteScore
7.80
自引率
5.40%
发文量
36
期刊介绍: Aims and Scope The International Journal of Multimedia Information Retrieval (IJMIR) is a scholarly archival journal publishing original, peer-reviewed research contributions. Its editorial board strives to present the most important research results in areas within the field of multimedia information retrieval. Core areas include exploration, search, and mining in general collections of multimedia consisting of information from the WWW to scientific imaging to personal archives. Comprehensive review and survey papers that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant. Relevant topics include Image and video retrieval - theory, algorithms, and systems Social media interaction and retrieval - collaborative filtering, social voting and ranking Music and audio retrieval - theory, algorithms, and systems Scientific and Bio-imaging - MRI, X-ray, ultrasound imaging analysis and retrieval Semantic learning - visual concept detection, object recognition, and tag learning Exploration of media archives - browsing, experiential computing Interfaces - multimedia exploration, visualization, query and retrieval Multimedia mining - life logs, WWW media mining, pervasive media analysis Interactive search - interactive learning and relevance feedback in multimedia retrieval Distributed and high performance media search - efficient and very large scale search Applications - preserving cultural heritage, 3D graphics models, etc. Editorial Policies: We aim for a fast decision time (less than 4 months for the initial decision) There are no page charges in IJMIR. Papers are published on line in advance of print publication. Academic, industrial researchers, and practitioners involved with multimedia search, exploration, and mining will find IJMIR to be an essential source for important results in the field.
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