School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Comput Intell Neurosci. 2022 May 14;2022:4316163. doi: 10.1155/2022/4316163. eCollection 2022.
Aiming at the problems of small key space, low security, and low algorithm complexity in a low-dimensional chaotic system encryption algorithm, an image encryption algorithm based on the ML neuron model and DNA dynamic coding is proposed. The algorithm first performs block processing on the R, G, and B components of the plaintext image to obtain three matrices, and then constructs a random matrix with the same size as the image components through logistic mapping and performs DNA encoding, DNA operation, and DNA decoding on the two parts. Second, it performs determinant permutation on the matrix by two different chaotic sequences obtained by logistic mapping iteration. Finally, it merges the block and image components to complete the image encryption and obtain the ciphertext image. Wherein, DNA encoding, DNA operation, and DNA decoding methods are all randomly and dynamically determined by the chaotic sequence generated by the ML neuron chaotic system. According to simulation results and performance analysis, the algorithm has a larger key space, can effectively resist various statistical and differential attacks, and has better security and higher complexity.
针对低维混沌系统加密算法中密钥空间小、安全性低、算法复杂度低的问题，提出了一种基于 ML 神经元模型和 DNA 动态编码的图像加密算法。该算法首先对明文图像的 R、G、B 分量进行分块处理，得到三个矩阵，然后通过 logistic 映射构造与图像分量大小相同的随机矩阵，并对两部分进行 DNA 编码、DNA 运算和 DNA 解码。其次，通过 logistic 映射迭代得到的两个不同混沌序列对矩阵进行行列式置换。最后，合并分块和图像分量，完成图像加密，得到密文图像。其中，DNA 编码、DNA 运算和 DNA 解码方法均由 ML 神经元混沌系统产生的混沌序列随机动态确定。根据仿真结果和性能分析，该算法具有更大的密钥空间，能够有效抵抗各种统计和差分攻击，具有较好的安全性和较高的复杂度。
