SE-NDEND: A novel symmetric watermarking framework with neural network-based chaotic encryption for Internet of Medical Things

Chai, Xiuli; Tang, Zongwei; Gan, Zhihua; Lu, Yang; Wang, Binjie; Zhang, Yushu

doi:10.1016/j.bspc.2023.105877

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…Nonlinear science is theoretically significant and promising for practical applications across various life aspects. Over the past decade, researchers have extensively applied it in fields such as image encryption [1][2][3][4][5], electronic circuits [6][7][8][9][10], chaos synchronization [11][12][13][14][15], pseudo-random number generators [16][17][18][19][20], and neural networks [21][22][23][24][25], among others. The complex structure of chaotic attractors enhances their dynamic properties.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis and Field-Programmable Gate Array Implementation of a 5D Fractional-Order Memristive Hyperchaotic System with Multiple Coexisting Attractors

Yu,

Zhang,

Xiao

et al. 2024

Fractal Fract

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On the basis of the chaotic system proposed by Wang et al. in 2023, this paper constructs a 5D fractional-order memristive hyperchaotic system (FOMHS) with multiple coexisting attractors through coupling of magnetic control memristors and dimension expansion. Firstly, the divergence, Kaplan–Yorke dimension, and equilibrium stability of the chaotic model are studied. Subsequently, we explore the construction of the 5D FOMHS, introducing the definitions of the Caputo differential operator and the Riemann–Liouville integral operator and employing the Adomian resolving approach to decompose the linears, the nonlinears, and the constants of the system. The complex dynamic characteristics of the system are analyzed by phase diagrams, Lyapunov exponent spectra, time-domain diagrams, etc. Finally, the hardware circuit of the proposed 5D FOMHS is performed by FPGA, and its randomness is verified using the NIST tool.

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