用新型神经元模型实现多场景图像加密，提升警务数据安全性。
A Novel Discrete Memristor-Coupled Heterogeneous Dual-Neuron Model and Its Application in Multi-Scenario Image Encryption
- 构建异构双神经元膜阻器耦合网络，模拟复杂生物放电行为。
- 在STM32平台验证，实现多场景实时图像加密，抗攻击能力增强。
- 适合公安、安防等对数据安全要求高的实时系统应用。
利用神经网络模拟脑功能是重要研究方向。离散膜阻器耦合神经元近年受到关注，因其能有效模拟突触行为，对学习与记忆具有生物学意义。本文提出一种离散膜阻器异构双神经元网络（MHDNN），分析其在初值和多种神经参数下的稳定性。数值仿真揭示复杂动态行为：不同耦合强度下出现多样放电模式，神经元间同步现象被深入研究。MHDNN已在STM32硬件平台上实现并验证。基于该模型，提出一种图像加密算法，并设计两个专用于多场景警务图像加密的硬件平台。该方案可实现复杂环境下警务数据的实时、安全传输，显著降低被黑客攻击风险，提升系统整体安全性。
原文摘要 · Abstract (English)
Simulating brain functions using neural networks is an important area of research. Recently, discrete memristor-coupled neurons have attracted significant attention, as memristors effectively mimic synaptic behavior, which is essential for learning and memory. This highlights the biological relevance of such models. This study introduces a discrete memristive heterogeneous dual-neuron network (MHDNN). The stability of the MHDNN is analyzed with respect to initial conditions and a range of neuronal parameters. Numerical simulations demonstrate complex dynamical behaviors. Various neuronal firing patterns are investigated under different coupling strengths, and synchronization phenomena between neurons are explored. The MHDNN is implemented and validated on the STM32 hardware platform. An image encryption algorithm based on the MHDNN is proposed, along with two hardware platforms tailored for multi-scenario police image encryption. These solutions enable real-time and secure transmission of police data in complex environments, reducing hacking risks and enhancing system security.
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