Zhang, Jing, Dai, Long, Xu, Liaoran ORCID: 0009-0001-4182-5979 , Ma, Jixin ORCID: 0000-0001-7458-7412 and Zhou, Xiaoyi (2023) Black-Box watermarking and blockchain for IP protection of voiceprint recognition model. Electronics, 12 (17):3697. pp. 1-16. ISSN 2079-9292 (Online) (doi:https://doi.org/10.3390/electronics12173697)

Preview

PDF (Publisher VoR) 44489_MA_Black_Box_watermarking_and_blockchain_for_IP_protection_of_voiceprint.pdf - Published Version Available under License Creative Commons Attribution. Download (6MB) | Preview

Abstract

Deep neural networks are widely used for voiceprint recognition, whilst voiceprint recognition models are vulnerable to attacks. Existing protection schemes for voiceprint recognition models are insufficient to withstand various robustness attacks and cannot prevent model theft. This paper proposes a black-box voiceprint recognition model protection framework that combines active and passive protection. It embeds key information into the Mel spectrogram to generate trigger samples that are difficult to detect and remove and injects them into the host model as watermark W, thereby enhancing the copyright protection performance of the voiceprint recognition model. To restrict the use of the model by unauthorized users, the index number corresponding to the model and the encrypted model information are stored on the blockchain, and then, an exclusive smart contract is designed to restrict access to the model. Experimental results show that this framework effectively protects voiceprint recognition model copyrights and restricts unauthorized access.

Item Type:	Article
Uncontrolled Keywords:	copyright protection; voiceprint recognition model; watermarking; blockchain; black-box; Mel spectrogram
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	18 Oct 2023 08:58
URI:	http://gala.gre.ac.uk/id/eprint/44489

Actions (login required)

View Item

Downloads