Advertisement

Impulsive Neural Networks Towards Image Protection

  • Zhi-Hong Guan
  • Bin Hu
  • Xuemin (Sherman) Shen
Chapter

Abstract

Inspired by security applications in the Industrial Internet of Things (IIoT), this chapter focuses on the usage of impulsive neural network synchronization technique for intelligent image protection against illegal swiping and abuse. A class of nonlinear interconnected neural networks with transmission delay and random impulse effect is first introduced. In order to make network protocols more flexible, a randomized broadcast impulsive coupling scheme is integrated into the protocol design. Impulsive synchronization criteria are then derived for the chaotic neural networks in presence of nonlinear protocol and random broadcast impulse, with the impulse effect discussed. Illustrative examples are provided to verify the developed impulsive synchronization results and to show its potential application in image encryption and decryption.

References

  1. 1.
    J. A. Stankovic, “Research directions for the Internet of Things,” IEEE Internet Things J., vol. 1, no. 1, pp. 3–9, 2014.MathSciNetCrossRefGoogle Scholar
  2. 2.
    A. R. Sadeghi, C. Wachsmann, and M. Waidner, “Security and privacy challenges in Industrial Internet of Things,” in 52nd ACM/EDAC/IEEE Design Automation Conf., 2015, pp. 1–6.Google Scholar
  3. 3.
    G. R. Chen and X. N. Dong, “From Chaos to Order: Methodologies, Perspectives and Applications,” Singapore: World Scientific, 1998.Google Scholar
  4. 4.
    G. Chen, Y. Mao, and C. K. Chui, “A symmetric image encryption scheme based on 3D chaotic cat maps,” Chaos, Solitons and Fractals, vol. 21, no. 3, pp. 749–761, 2004.MathSciNetCrossRefGoogle Scholar
  5. 5.
    E. Cherrier, M. Boutayeb, and J. Ragot, “Observers-based synchronization and input recovery for a class of nonlinear chaotic models,” IEEE Trans. Circuits Syst. I, vol. 53, no. 9, pp. 1977–1988, 2006.CrossRefGoogle Scholar
  6. 6.
    Z.-H. Guan, F. Huang, and W. Guan, “Chaos-based image encryption algorithm,” Phy. Lett. A, vol. 346, no. 1–3, pp. 153–157, 2005.CrossRefGoogle Scholar
  7. 7.
    A. Proskurnikov and M. Cao, “Synchronization of Goodwin’s oscillators under boundedness and nonnegativeness constraints for solutions,” IEEE Trans. Autom. Contr., vol. 62, no. 1, pp. 372–378, 2017.MathSciNetCrossRefGoogle Scholar
  8. 8.
    W. Lu and T. Chen, “Synchronization of coupled connected neural networks with delays,” IEEE Trans. Circuits Syst. I, vol. 54, no. 6, pp. 1317–1326, 2004.Google Scholar
  9. 9.
    C. Yang, Y. Jiang, Z. Li, W. He, and C. Su, “Neural control of bimanual robots with guaranteed global stability and motion precision,” IEEE Trans. Ind. Informat., vol. 13, no. 3, pp. 1162–1171, 2017.CrossRefGoogle Scholar
  10. 10.
    O. Machado, P. M. Sanchez, F. J. Rodriguez, and E. Bueno, “A neural network-based dynamic cost function for the implementation of a predictive current controller,” IEEE Trans. Ind. Informat., vol. 13, no. 6, pp. 2946–2955, 2017.CrossRefGoogle Scholar
  11. 11.
    D. Chen, “Research on traffic flow prediction in the big data environment based on the improved RBF neural network,” IEEE Trans. Ind. Informat., vol. 13, no. 4, pp. 2000–2008, 2017.CrossRefGoogle Scholar
  12. 12.
    J. Lu, D. W. C. Ho, and J. Cao, “A unified synchronization criterion for impulsive dynamical networks,” Automatica, vol. 46, no. 7, pp. 1215–1221, 2010.MathSciNetCrossRefGoogle Scholar
  13. 13.
    B. Hu, Z.-H. Guan, T.-H. Qian, and G. Chen, “Dynamic analysis of hybrid impulsive delayed neural networks with uncertainties,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 9, pp. 4370–4384, 2018.CrossRefGoogle Scholar
  14. 14.
    S. Boccaletti, J. Kurths, G. Osipov, D. L. Valladares, and C. S. Zhou, “The synchronization of chaotic systems,” Phy. Reports, vol. 366, no. 1–2, pp. 1–101, 2002.MathSciNetzbMATHGoogle Scholar
  15. 15.
    A. Arenas, A. Diaz-Guilera, J. Kurths, Y. Morenob, and C. Zhou, “Synchronization in complex networks,” Phy. Reports, vol. 469, no. 3, pp. 93–153, 2008.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Z. Wu, P. Shi, H. Su, and J. Chu, “Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled data,” IEEE Trans. Cybern., vol. 43, no. 6, pp. 1796–1806, 2013.CrossRefGoogle Scholar
  17. 17.
    K. Gopalsamy and I. Leung, “Delay induced periodicity in a neural netlet of excitation and inhibition,” Physica D, vol. 89, no. 3–4, pp. 395–426, 1996.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Y. Liu, Z. Wang, J. Liang, and X. Liu, “Synchronization of coupled neutral-type neural networks with jumping-mode-dependent discrete and unbounded distributed delays,” IEEE Trans. Cybern., vol. 43, no. 1, pp. 102–114, 2013.CrossRefGoogle Scholar
  19. 19.
    M. Stern, H. Sompolinsky, and L. F. Abbott, “Dynamics of random neural networks with bistable units,” Phy. Rev. E, vol. 90, no. 062710, pp. 1–7, 2014.Google Scholar
  20. 20.
    Z.-H. Guan, Z.-W. Liu, G. Feng, and Y.-W. Wang, “Synchronization of complex dynamical networks with time-varying delays via impulsive distributed control,” IEEE Trans. Circuits Syst. I, vol. 57, no. 8, pp. 2182–2195, 2010.MathSciNetCrossRefGoogle Scholar
  21. 21.
    Z.-H. Guan, B. Hu, M. Chi, D.-X. He, and X.-M. Cheng, “Guaranteed performance consensus in second-order multi-agent systems with hybrid impulsive control,” Automatica, vol. 50, no. 9, pp. 2415–2418, 2014.MathSciNetCrossRefGoogle Scholar
  22. 22.
    W. K. Wong, W. Zhang, Y. Tang, and X. Wu, “Stochastic synchronization of complex networks with mixed impulses,” IEEE Trans. Circuits Syst. I, vol. 60, no. 10, pp. 2657–2667, 2013.MathSciNetCrossRefGoogle Scholar
  23. 23.
    W.-H. Chen, S. Luo, and W. X. Zheng, “Impulsive synchronization of reaction-diffusion neural networks with mixed delays and its application to image encryption,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 12, pp. 2696–2710, 2016.CrossRefGoogle Scholar
  24. 24.
    A. Mosebach and J. Lunze, “A deterministic gossiping algorithm for the synchronization of multi-agent systems,” in 5th IFAC Workshop on Distributed Estimation and Control in Netw. Syst., 2015, pp. 1–7.Google Scholar
  25. 25.
    S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Randomized gossip algorithms,” IEEE Trans. Inform. Theory, vol. 52, no. 6, pp. 2508–2530, 2006.MathSciNetCrossRefGoogle Scholar
  26. 26.
    T. Aysal, M. Yildiz, A. Sarwate, and A. Scaglione, “Broadcast gossip algorithms for consensus,” IEEE Trans. Signal Process., vol. 57, no. 7, pp. 2748–2761, 2009.MathSciNetCrossRefGoogle Scholar
  27. 27.
    J.-L. Wang, H.-N. Wu, T. Huang, S.-Y. Ren, and J. Wu, “Pinning control for synchronization of coupled reaction-diffusion neural networks with directed topologies,” IEEE Trans. Syst., Man, Cybern., Syst., vol. 46, no. 8, pp. 1109–1120, 2016.CrossRefGoogle Scholar
  28. 28.
    X. Liu and T. Chen, “Synchronization of nonlinear coupled networks via aperiodically intermittent pinning control,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 1, pp. 113–126, 2015.MathSciNetCrossRefGoogle Scholar
  29. 29.
    H. Lu, “Chaotic attractors in delayed neural networks,” Phys. Lett. A, vol. 298, no. 2–3, pp. 109–116, 2002.CrossRefGoogle Scholar
  30. 30.
    L. Moreau, “Stability of multiagent systems with time-dependent communication links,” IEEE Trans. Autom. Contr., vol. 50, no. 2, pp. 169–182, 2005.MathSciNetCrossRefGoogle Scholar
  31. 31.
    J. P. Hespanha and A. S. Morse, “Stability of switched systems with average dwell-time,” in 38th IEEE conf. decision contr., 1999, pp. 2655–2660.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhi-Hong Guan
    • 1
  • Bin Hu
    • 2
  • Xuemin (Sherman) Shen
    • 3
  1. 1.College of AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.Wuhan National Laboratory For OptoelectronicsHuazhong University of Science and TechnologyWuhanChina
  3. 3.Electrical and Computer Engineering DepartmentUniversity of WaterlooWaterlooCanada

Personalised recommendations