中国物理B ›› 2025, Vol. 34 ›› Issue (5): 50502-050502.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

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)   

  1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
  • 收稿日期:2024-12-16 修回日期:2025-02-06 接受日期:2025-02-21 出版日期:2025-05-15 发布日期:2025-04-18
  • 通讯作者: Fei Yu E-mail:yufeiyfyf@csust.edu.cn
  • 基金资助:
    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).

Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection

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)   

  1. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
  • Received:2024-12-16 Revised:2025-02-06 Accepted:2025-02-21 Online:2025-05-15 Published:2025-04-18
  • Contact: Fei Yu E-mail:yufeiyfyf@csust.edu.cn
  • Supported by:
    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).

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摘要： 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.

关键词: memristor, hyperchaos, resonant tunneling diode-based cellular neural network (RTD-CNN), dynamic analysis, image encryption

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.

Key words: memristor, hyperchaos, resonant tunneling diode-based cellular neural network (RTD-CNN), dynamic analysis, image encryption

中图分类号:  (Nonlinear dynamics and chaos)

  • 05.45.-a
05.45.Gg (Control of chaos, applications of chaos) 05.45.Jn (High-dimensional chaos) 05.45.Vx (Communication using chaos)