基于脉冲神经网络变体的并行二值图像密码系统。

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
International Journal of Neural Systems Pub Date : 2022-08-01 Epub Date: 2021-02-26 DOI:10.1142/S0129065721500143
Mingzhe Liu, Feixiang Zhao, Xin Jiang, Hong Zhang, Helen Zhou
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引用次数: 5

摘要

针对多幅二值图像加密效率低的问题,提出了一种基于尖峰神经网络的典型变体——尖峰神经P (SNP)系统的并行二值图像加密框架。更具体地说,所提出的图像密码系统中的两个基本单元,排列单元和扩散单元,分别通过具有多通道和极化的SNP系统(SNP- mcp系统)和具有星形细胞样控制的SNP系统(SNP- alc系统)设计。与传统图像排列/扩散单元的串行计算不同,基于snp - mcp的排列/ snp - alc的扩散单元可以通过神经元内部规则的并行利用来实现并行计算。理论分析结果证实了所提出的二值图像密码系统的高效性。安全分析实验证明了所提出密码系统的安全性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel Binary Image Cryptosystem Via Spiking Neural Networks Variants.

Due to the inefficiency of multiple binary images encryption, a parallel binary image encryption framework based on the typical variants of spiking neural networks, spiking neural P (SNP) systems is proposed in this paper. More specifically, the two basic units in the proposed image cryptosystem, the permutation unit and the diffusion unit, are designed through SNP systems with multiple channels and polarizations (SNP-MCP systems), and SNP systems with astrocyte-like control (SNP-ALC systems), respectively. Different from the serial computing of the traditional image permutation/diffusion unit, SNP-MCP-based permutation/SNP-ALC-based diffusion unit can realize parallel computing through the parallel use of rules inside the neurons. Theoretical analysis results confirm the high efficiency of the binary image proposed cryptosystem. Security analysis experiments demonstrate the security of the proposed cryptosystem.

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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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