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

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

2022IEEE Transactions on Neural Networks and Learning Systems21 citationsDOI

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.

Topics & Concepts

RGB color modelSynchronization (alternating current)Computer visionComputer scienceArtificial intelligenceEncryptionArtificial neural networkImage (mathematics)Stability (learning theory)TelecommunicationsComputer networkChannel (broadcasting)Machine learningNeural Networks Stability and SynchronizationMathematical Biology Tumor GrowthAdvanced Memory and Neural Computing