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<p>The memristor’s inherent memristive and nonlinear properties make it particularly well-suited for simulating synaptic connections in neural networks, inducing rich dynamical behaviors that are essential for understanding brain mechanisms and brain-like learning. In this paper, a new locally active non-volatile trigonometric memristor is constructed and coupled into the Hopfield neural network. The motion state of the memristive Hopfield neural network (MHNN) is influenced by the coupling strength, allowing it to exhibit periodic initial offset boosting behavior. Furthermore, the MHNN demonstrates unique multi-scroll attractor extension behaviors. The number of attractor scrolls increases continuously with parameters variations at fixed time intervals, although there is an upper limit. However, as simulation time extends, the number of attractor scrolls can grow indefinitely, with newly formed scrolls extending monotonically in both directions under different conditions. The MHNN is implemented using both analog circuits and the DSP platform. Eventually, a real-time image encryption scheme aimed at protecting medical image privacy is designed, supported by practical test. In particular, the scheme can be further applied to remote video medical protection, which can encrypt the treatment content.</p>

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Real-time dynamic medical image encryption based on extended multi-scroll memristive Hopfield neural network

  • 期刊论文

  • 发表日期：

    2025年1月23日

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  • Xiangxin Leng,
  • Xiaoping Wang,
  • Baoxiang Du,
  • Fangmin Ren,
  • Zhigang Zeng

摘要

The memristor’s inherent memristive and nonlinear properties make it particularly well-suited for simulating synaptic connections in neural networks, inducing rich dynamical behaviors that are essential for understanding brain mechanisms and brain-like learning. In this paper, a new locally active non-volatile trigonometric memristor is constructed and coupled into the Hopfield neural network. The motion state of the memristive Hopfield neural network (MHNN) is influenced by the coupling strength, allowing it to exhibit periodic initial offset boosting behavior. Furthermore, the MHNN demonstrates unique multi-scroll attractor extension behaviors. The number of attractor scrolls increases continuously with parameters variations at fixed time intervals, although there is an upper limit. However, as simulation time extends, the number of attractor scrolls can grow indefinitely, with newly formed scrolls extending monotonically in both directions under different conditions. The MHNN is implemented using both analog circuits and the DSP platform. Eventually, a real-time image encryption scheme aimed at protecting medical image privacy is designed, supported by practical test. In particular, the scheme can be further applied to remote video medical protection, which can encrypt the treatment content.