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A Novel Image Encryption Algorithm Based on Hybrid Chaotic Mapping and Intelligent Learning in Financial Security System

机译:一种基于混合混沌映射和智能学习金融安全系统的新型图像加密算法

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摘要

With the expansion and prevalence of financial certification, problems of financial security have been springing up. How to ensure the security of financial information and protect our privacy is an issue of particular concern. In order to reduce the influence of chaotic periodicity on cipher-text, an image dynamic encryption algorithm based on hybrid chaotic system and deep network is proposed in this paper. Firstly, the hybrid chaotic system is constructed to combine many mapping functions by using the nonlinear combination mechanism, and the chaotic sequence is outputted to generate the initial value of hybrid chaotic system by using the pixel value. Then the plain-text pixel values are adopted to generate the initial value of the hybrid system for improving the anti-plain attack ability of the algorithm; Artificial neural network is used to process chaotic sequences, and effectively eliminate chaotic periodicity; The classification and encryption of permutation image are realized by constructing quantization method and hetero-diffusion technology. The experimental results show that our proposed encryption technology has higher security and stronger ability to resist plain-text attack compared with the existing image encryption algorithms, which adapts to enhance the security of the financial system.
机译:随着财务认证的扩展和普遍性,金融安全问题一直在涌现。如何确保财务信息的安全性和保护我们的隐私是一个特别关注的问题。为了减少混沌周期对密文的影响,本文提出了一种基于混合混沌系统和深网络的图像动态加密算法。首先,构造混合混沌系统以通过使用非线性组合机制来组合许多映射功能,并且通过使用像素值来输出混沌序列以产生混合混沌系统的初始值。然后采用纯文本像素值来生成混合系统的初始值,以提高算法的抗普通攻击能力;人工神经网络用于处理混沌序列,有效地消除混沌周期性;通过构建量化方法和异质扩散技术来实现置换图像的分类和加密。实验结果表明,与现有的图像加密算法相比,我们所提出的加密技术具有更高的安全性和更强的抵抗普通文本攻击的能力,适应增强金融系统的安全性。

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