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混沌神经元耦合置乱神经元的图像加密算法研究 被引量:4

A New Secure Image Encryption Algorithm Based on Chaotic Neuron Layer and Permutation Neuron Layer
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摘要 目的为了使当前加密系统具有更强的密钥敏感性以及更大的密钥空间,以提高抗各种攻击性能。方法提出一种新型的基于置乱神经元耦合混沌神经元的图像加密算法。加密系统的置乱和扩散由2个不同的3层神经构成,分别是置乱神经元层和混沌神经元层,混沌密钥生成模块则通过相应的权值和偏置来对这2层结构进行控制。在混沌神经元层扩散过程中,3个混沌系统用来生成权值矩阵和偏置矩阵,通过非线性标准化、按位异或操作来进行非线性组合,并通过Tent映射来进行激活,以获得扩散信息。在置乱神经元层置乱过程中,利用混沌密钥生成模块获取置乱矩阵,对扩散信息进行线性置乱处理,再通过二维Cat混沌映射对信息进行非线性置乱处理,并与当前加密算法进行对比。结果与当前加密算法相比,文中算法安全性更高,平均熵值为7.9991,且该加密算法的密钥空间大,为2160×1060,密钥敏感性强,错误与正确密钥之间的密文差异率为99.765%。结论设计的加密算法高度安全,可有效抗击各种攻击。 Objective In order to obtain a stronger key encryption system and greater sensitivity to key space to improve the performance of the anti-attacks. Methods In this paper, a novel block encryption algorithm based on chaotic neural networks was proposed. The permutation and diffusion of the encryption system consisted of two 3- neurons layers, which were permutation neuron layer and chaotic neuron layer, and the two layers were controlled by a chaotic key generator block through corresponding weights and biases. In the chaotic neuron layer, three chaotic systems were used to generate the weights matrices and biases matrices, and through standardization and nonlinear Bit-wise XOR operation for non-linear combination, as well as a chaotic tent map is employed as the activation function of this layer; In the permutation neurons layers, and the key generation block was used to generate chaotic scrambling matrix which was applied to obtain diffused information, then a two-dimensional Cat chaotic map was applied to the data to obtain three-dimensional permutation. Results Compared with the existing encryption algorithm, the algorithm proposed in this paper was more secure, with an average entropy of 7.9991, the key space of this encryption algorithm was as large as 2160× 1060, and the sensitivity was high, the cipher text error rate between the wrong and correct keys was 99.765%. Conclusion Simulation results showed that the image encryption algorithm was highly secure and can effectively resist various attacks.
作者 田玉萍
机构地区 黑河学院
出处 《包装工程》 CAS CSCD 北大核心 2014年第15期105-112,共8页 Packaging Engineering
关键词 图像加密 混沌神经元 置乱神经元层 权值矩阵 偏置矩阵 抗穷举攻击 image encryption chaotic neural network permutation neuron layer weighted matrix bias matrix brute-force attacks
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