Protecting the intellectual property of deep neural networks with watermarking: The frequency domain approach

Li, Meng, Zhong, Qi, Zhang, Leo Yu, Du, Yajuan, Zhang, Jun and Xiang, Yong 2021, Protecting the intellectual property of deep neural networks with watermarking: The frequency domain approach, in TrustCom 2020 : Proceedings of IEEE's 19th International Conference on Trust, Security and Privacy in Computing and Communications, IEEE Computer Society, Los Alamitos, Calif., pp. 402-409, doi: 10.1109/TrustCom50675.2020.00062.

Attached Files
Name Description MIMEType Size Downloads

Title Protecting the intellectual property of deep neural networks with watermarking: The frequency domain approach
Author(s) Li, Meng
Zhong, Qi
Zhang, Leo YuORCID iD for Zhang, Leo Yu orcid.org/0000-0001-9330-2662
Du, Yajuan
Zhang, Jun
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Conference name TrustCom 2020. Trust, Security and Privacy in Computing and Communications. IEEE International Conference (19th : 2020 : Guangzhou, China)
Conference location Guangzhou, China (part-virtually)
Conference dates 29 Dec 2020 - 01 Jan 2021
Title of proceedings TrustCom 2020 : Proceedings of IEEE's 19th International Conference on Trust, Security and Privacy in Computing and Communications
Editor(s) Wang, Guojun
Ko, Ryan
Alam Bhuiyan, Md Zakirul
Pan, Yi
Publication date 2021
Start page 402
End page 409
Total pages 8
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) deep neural networks
frequency transform
intellectual property
watermarking
CORE2020 A
Notes DOI Not Found : Error https://doi.org/10.1109/TrustCom50675.2020.00062
ISBN 9780738143804
ISSN 2324-898X
2324-9013
Language eng
DOI 10.1109/TrustCom50675.2020.00062
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2020, Institute of Electrical and Electronics Engineers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30148599

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 12 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Thu, 04 Mar 2021, 14:05:13 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.