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A data encryption model based on intertwining logistic map

机译:基于交互逻辑图的数据加密模型

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摘要

A data encryption model is developed using Brownian Motion based confusion and intertwining logistic map based diffusion method. In the existing encryption models, shuffling is irrelevant for the pixels with same value i.e. (all white/black/same colour) and models are prone to differential and various statistical attacks. This article investigates random data insertion to ensure the validity of shuffling engine and result in a different cipher in each execution. It makes both differential and chosen plaintext attacks infeasible and reduces the correlation among existing image pixels. Further, an intertwining logistic map is used not only for better random number distribution but also to overcome the blank window noticed in the bifurcation diagram of logistic map. Brownian motion based confusion introduced key sensitivity in the encryption model. In last, Intertwining logistic map based diffusion model is applied so that even a single bit change affects most of the pixels in the cipher. Number of pixel change rate (NPCR) and Unified average change intensity (UACI) scores support the security enhancement of the model. Randomness is supported by the NIST randomness test. Simulation results show that the model has better security level, lossless compression, resistive against chosen and known plaintext attacks.
机译:基于布朗运动的混乱和基于交互的扩散方法,开发了数据加密模型。在现有的加密模型中,Shuffling对具有相同值的像素无关,(所有白色/黑/相同颜色)和型号容易出现差异和各种统计攻击。本文调查随机数据插入,以确保播种引擎的有效性并导致每次执行中的不同密码。它使差异和选择的明文攻击不可行,减少了现有图像像素之间的相关性。此外,交互逻辑图不仅用于更好的随机数分布,而且还用于克服Logistic地图的分叉图中注意到的空白窗口。布朗运动基于杂志引入了加密模型中的关键敏感性。最后,应用基于交互的基于逻辑地图的扩散模型,使得即使是单位改变也会影响密码中的大部分像素。像素变化率(NPCR)和统一平均变化强度(UACI)分数支持模型的安全增强。 NIST随机性测试支持随机性。仿真结果表明,该模型具有更好的安全水平,无损压缩,电阻对抗所选和已知的明文攻击。

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