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A Noise-Robust Image Encryption Algorithm Based on Hyper Chaotic Cellular Neural Network

机译:基于超混沌细胞神经网络的鲁棒图像加密算法

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摘要

We propose an image encryption algorithm based on a 6-dimensional chaotic cellular neural network (CNN) that is robust to noise/missing pixels in the cipher image. We performed parameter search on the templates of the CNN in order to discover the parameters that leads to 6D chaotic evolution of the state, and then used the resulting chaotic sequence as the basis of encryption. The encryption process itself consists of shuffling the positions of image pixels based on the numerical value of the chaotic sequence; the second half of the encryption process consists of changing the shuffled image pixel values by performing XOR operation between the pixel values and the numerical value of the chaotic sequence. By using simple operations like sorting and XOR in the encryption process, the algorithm is robust to noise/ missing pixels in the cipher image. We illustrate this by comparing the robustness against 3 recently proposed chaos-based image encryption algorithms. The results show that our algorithm is competitive with the state-of-the-art in term of encryption security, and superior in term of robustness.
机译:我们提出了一种基于6维混沌细胞神经网络(CNN)的图像加密算法,该算法对密码图像中的噪声/缺失像素具有鲁棒性。我们在CNN的模板上执行参数搜索,以发现导致状态6D混沌演化的参数,然后将所得的混沌序列用作加密的基础。加密过程本身包括根据混沌序列的数值对图像像素的位置进行改组。加密过程的后半部分包括通过在像素值和混沌序列的数值之间执行XOR操作来更改混洗后的图像像素值。通过在加密过程中使用诸如分类和XOR之类的简单操作,该算法对于加密图像中的噪声/丢失像素具有鲁棒性。我们通过比较3种最近提出的基于混沌的图像加密算法的鲁棒性来说明这一点。结果表明,我们的算法在加密安全性方面与最新技术竞争,并且在鲁棒性方面优越。

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