Finite-Time Synchronization of Neural Networks With Proportional Delays for RGB-D Image Protection. Academic Article

abstract

• Since the depth information of images facilitates the analysis of the spatial distance of objects in computer vision applications, it is necessary to protect the image depth information. Thus this article proposes a novel red-green-blue-depth (RGB-D) image protection algorithm, which is implemented with the finite-time synchronization (FTS) of neural networks (NNs) with proportional delays via the quantized intermittent control to derive the system synchronization criterion based on Lyapunov stability theory. The performance of RGB-D image protection depends on the synchronization error of the system by driving the system sequence to encrypt the RGB-D image and responding to the system sequence to decrypt the encrypted image. Subsequently, the validity of the proposed criteria is verified by simulation examples, and the practical application of RGB-D image protection is verified.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Yang, W., Huang, J., He, X., Wen, S., & Huang, T.

citation count

• 0

complete list of authors

• Yang, Wenqiang||Huang, Junjian||He, Xing||Wen, Shiping||Huang, Tingwen

publication date

• December 2022

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2162-237XISSN  Journal

keywords

• Finite-time Synchronization (fts)
• Image Protection
• Proportional Delay
• Quantized Intermittent Controller
• Red-green-blue-depth (rgb-d)

PubMed Central ID

• 37015529

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2022.3225164

start page

• 1

end page

• 12

volume

• PP

issue

• 99

URL

• http://dx.doi.org/10.1109/tnnls.2022.3225164