Fixed-time synchronization of complex-valued neural networks for image protection and 3D point cloud information protection.

Yang, W; Huang, J; He, X; Wen, S

Fixed-time synchronization of complex-valued neural networks for image protection and 3D point cloud information protection.

Yang, W Huang, J He, X Wen, S

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Neural Netw, 2023, 172, pp. 106089
Issue Date:: 2023-12-27

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Field	Value	Language
dc.contributor.author	Yang, W
dc.contributor.author	Huang, J
dc.contributor.author	He, X
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-01-16T21:05:48Z
dc.date.available	2023-12-22
dc.date.available	2024-01-16T21:05:48Z
dc.date.issued	2023-12-27
dc.identifier.citation	Neural Netw, 2023, 172, pp. 106089
dc.identifier.issn	0893-6080
dc.identifier.issn	1879-2782
dc.identifier.uri	http://hdl.handle.net/10453/174614
dc.description.abstract	This paper studies the fixed-time synchronization (FDTS) of complex-valued neural networks (CVNNs) based on quantized intermittent control (QIC) and applies it to image protection and 3D point cloud information protection. A new controller was designed which achieved FDTS of the CVNNs, with the estimation of the convergence time not dependent on the initial state. Our approach divides the neural network into two real-valued systems and then combines the framework of the Lyapunov method to give criteria for FDTS. Applying synchronization to image protection, the image will be encrypted with a drive system sequence and decrypted with a response system sequence. The quality of image encryption and decryption depends on the synchronization error. Meanwhile, the depth image of the object is encrypted and then the 3D point cloud is reconstructed based on the decrypted depth image. This means that the 3D point cloud information is protected. Finally, simulation examples verify the efficacy of the controller and the synchronization criterion, giving results for applications in image protection and 3D point cloud information protection.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Neural Netw
dc.relation.isbasedon	10.1016/j.neunet.2023.12.043
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4611 Machine learning
dc.subject.classification	4905 Statistics
dc.title	Fixed-time synchronization of complex-valued neural networks for image protection and 3D point cloud information protection.
dc.type	Journal Article
utslib.citation.volume	172
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-16T21:05:47Z
pubs.publication-status	Published online
pubs.volume	172

Abstract:

This paper studies the fixed-time synchronization (FDTS) of complex-valued neural networks (CVNNs) based on quantized intermittent control (QIC) and applies it to image protection and 3D point cloud information protection. A new controller was designed which achieved FDTS of the CVNNs, with the estimation of the convergence time not dependent on the initial state. Our approach divides the neural network into two real-valued systems and then combines the framework of the Lyapunov method to give criteria for FDTS. Applying synchronization to image protection, the image will be encrypted with a drive system sequence and decrypted with a response system sequence. The quality of image encryption and decryption depends on the synchronization error. Meanwhile, the depth image of the object is encrypted and then the 3D point cloud is reconstructed based on the decrypted depth image. This means that the 3D point cloud information is protected. Finally, simulation examples verify the efficacy of the controller and the synchronization criterion, giving results for applications in image protection and 3D point cloud information protection.

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http://hdl.handle.net/10453/174614