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.

使用基于布朗运动的混淆和基于交织逻辑图的扩散方法开发了数据加密模型。在现有的加密模型中，改组与具有相同值（即，所有白色/黑色/相同颜色）的像素无关，并且模型易于受到差分和各种统计攻击。本文研究随机数据插入，以确保改组引擎的有效性并在每次执行中产生不同的密码。这使得差分和选择的明文攻击都不可行，并降低了现有图像像素之间的相关性。此外，缠结的逻辑图不仅用于更好的随机数分布，而且还用于克服逻辑图分叉图中注意到的空白窗口。基于布朗运动的混淆在加密模型中引入了密钥敏感性。最后，应用了基于交织逻辑图的扩散模型，因此，即使单个位的更改也会影响密码中的大多数像素。像素变化率（NPCR）数量和统一平均变化强度（UACI）分数支持模型的安全性增强。NIST随机性测试支持随机性。仿真结果表明，该模型具有较好的安全级别，无损压缩，能够抵抗选择的和已知的明文攻击。