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A novel approach for secure compressive sensing of images using multiple chaotic maps

机译：一种使用多个混沌映射的图像安全压缩感测的新方法

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摘要

In this paper, a novel approach for secure compressive sensing of images based on multiple one dimensional chaotic maps is proposed. The basic idea is to perform the random selection of a combination of two one dimensional chaotic maps to generate a random stream. One or more values from the random stream are used to generate each normal value in the random measurement matrix for compressive sensing. In the proposed approach, eight different one dimensional chaotic maps are used. For one measurement matrix generation, two of them are randomly selected based on a secret key. The number of iterations and the initial states of the chaotic maps are also decided by external secret keys. The chaotic output of iterations of the two selected chaotic maps are XORed to generate a new chaotic value so that the measurement matrix so generated can withstand known plaintext attack. The block-based compressive sensing (BCS) of images is adopted to validate the proposed system. Further enhancement in the security of the proposed system for BCS of images can be obtained by using different measurement matrices for different blocks of images. An algorithm for generating multiple measurement matrices is also presented in this work. It is experimentally proved that the proposed encryption system can maintain the robustness to noise of the compressive sensing system. The proposed robust encryption system is subjected to several forms of attacks and is proved to be resistant against all.
机译：本文提出了一种基于多维一维混沌图的图像安全压缩感知新方法。基本思想是对两个一维混沌图的组合进行随机选择以生成随机流。来自随机流的一个或多个值用于生成随机测量矩阵中的每个正常值以进行压缩感测。在提出的方法中，使用了八个不同的一维混沌图。对于一个测量矩阵的生成，基于秘密密钥随机选择其中两个。混沌图的迭代次数和初始状态也由外部密钥决定。对两个选定的混沌图的迭代的混沌输出进行异或运算以生成新的混沌值，从而使生成的测量矩阵可以承受已知的明文攻击。采用基于块的图像压缩感知（BCS）来验证所提出的系统。通过针对不同的图像块使用不同的测量矩阵，可以进一步提高所提出的用于图像的BCS的系统的安全性。在这项工作中还提出了一种用于生成多个测量矩阵的算法。实验证明，所提出的加密系统可以保持压缩感测系统对噪声的鲁棒性。提出的健壮的加密系统受到多种形式的攻击，并被证明可以抵抗所有攻击。

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