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Fei Yu(余飞)†, Dan Su(苏丹), Shaoqi He(何邵祁), Yiya Wu(吴亦雅), Shankou Zhang(张善扣), and Huige Yin(尹挥戈). 2025: Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection, Chinese Physics B, 34(5): 050502. doi: 10.1088/1674-1056/adb8bb
Citation: Fei Yu(余飞)†, Dan Su(苏丹), Shaoqi He(何邵祁), Yiya Wu(吴亦雅), Shankou Zhang(张善扣), and Huige Yin(尹挥戈). 2025: Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection, Chinese Physics B, 34(5): 050502. doi: 10.1088/1674-1056/adb8bb
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Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection

  • School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China

  • Received Date: 16/12/2024
    Accepted Date: 06/02/2025
  • Fund Project:

    Project supported by the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 24A0248), the National Key Research and Development Program “National Quality Infrastructure System” Special Project (Grant No. 2024YFF0617900), and the Hefei Minglong Electronic Technology Co., Ltd. (Grant Nos. 2024ZKHX293, 2024ZKHX294, and 2024ZKHX295).

  • PACS: 05.45.-a; 05.45.Gg; 05.45.Jn; 05.45.Vx

  • Due to their biological interpretability, memristors are widely used to simulate synapses between artificial neural networks. As a type of neural network whose dynamic behavior can be explained, the coupling of resonant tunneling diode-based cellular neural networks (RTD-CNNs) with memristors has rarely been reported in the literature. Therefore, this paper designs a coupled RTD-CNN model with memristors (RTD-MCNN), investigating and analyzing the dynamic behavior of the RTD-MCNN. Based on this model, a simple encryption scheme for the protection of digital images in police forensic applications is proposed. The results show that the RTD-MCNN can have two positive Lyapunov exponents, and its output is influenced by the initial values, exhibiting multistability. Furthermore, a set of amplitudes in its output sequence is affected by the internal parameters of the memristor, leading to nonlinear variations. Undoubtedly, the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy protection. Encryption tests and security analyses validate the effectiveness of this scheme.
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Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection

  • Received Date: 16/12/2024
  • Accepted Date: 06/02/2025
Fund Project: 

Abstract: Due to their biological interpretability, memristors are widely used to simulate synapses between artificial neural networks. As a type of neural network whose dynamic behavior can be explained, the coupling of resonant tunneling diode-based cellular neural networks (RTD-CNNs) with memristors has rarely been reported in the literature. Therefore, this paper designs a coupled RTD-CNN model with memristors (RTD-MCNN), investigating and analyzing the dynamic behavior of the RTD-MCNN. Based on this model, a simple encryption scheme for the protection of digital images in police forensic applications is proposed. The results show that the RTD-MCNN can have two positive Lyapunov exponents, and its output is influenced by the initial values, exhibiting multistability. Furthermore, a set of amplitudes in its output sequence is affected by the internal parameters of the memristor, leading to nonlinear variations. Undoubtedly, the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy protection. Encryption tests and security analyses validate the effectiveness of this scheme.

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