基于动态网络的非线性置乱扩散同步图像加密

具有置乱-扩散结构的传统图像加密中的置乱与扩散通常相互独立，易被单独针对破解，且加密过程非线性弱，导致算法安全性差，为此提出了一种具有强非线性的置乱扩散同步图像加密算法。首先，构造新型sine-cos混沌映射，以拓宽控制参数范围，并改善序列分布的随机性；然后，用明文像素与混沌序列的异或和作为混沌初始值产生混沌序列，用其构建不同明文的不同像素的网络结构，同时利用扩散值动态更新网络值，使网络具有动态性；最后，使用单像素串行置乱-扩散使置乱与扩散产生交叉作用，整体上达到置乱和扩散的同步，从而有效抵抗分离攻击。另外，依照网络结构转移像素操作，使串行路径具有非线性和不可预测性，从而保证算法的非线性与安全性，并且利用相邻节点像素和进行动态扩散，从而提高明文的相关性。实验结果表明，该算法加密安全性高，明文敏感性强，并在抗统计攻击、差分攻击、选择明文攻击等方面效果尤佳。

Nonlinear scrambling diffusion synchronization image encryption based on dynamic network

The traditional image encryption with scrambling-diffusion structure is usually divided into two independent steps of scrambling and diffusion， which are easy to be cracked separately， and the encryption process has weak nonlinearity， resulting in poor security of the algorithm. Therefore， a scrambling diffusion synchronous image encryption algorithm with strong nonlinearity was proposed. Firstly， a new sine-cos chaotic mapping was constructed to broaden the range of control parameters and improve the randomness of sequence distribution. Then， the exclusive-OR sum of plaintext pixels and chaotic sequence was used as the initial chaotic value to generate chaotic sequence， and this chaotic sequence was used to construct the network structures of different pixels of different plaintexts. At the same time， the diffusion value was used to dynamically update the network value to make the network dynamic. Finally， the single pixel serial scrambling-diffusion was used to generate cross-effect between scrambling and diffusion，and the overall synchronization of scrambling and diffusion， so as to effectively resist separation attacks. In addition， the pixel operations were transferred according to the network structure， which made the serial path nonlinear and unpredictable， thereby ensuring the nonlinearity and security of the algorithm. And the adjacent node pixels sum was used to perform dynamic diffusion in order to improve the correlation of the plaintext. Experimental results show that the proposed algorithm has high encryption security， strong plaintext sensitivity， and is particularly effective in anti-statistical attack， anti-differential attack and anti-plaintext attack.