Free Fine-tuning: A Plug-and-Play Watermarking Scheme for Deep Neural Networks-Reference-Cited by-同舟云学术

Free Fine-tuning: A Plug-and-Play Watermarking Scheme for Deep Neural Networks

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
language:
Short-container-title:

Author:

Wang Run¹^ORCID,Ren Jixing¹^ORCID,Li Boheng¹^ORCID,She Tianyi¹^ORCID,Zhang Wenhui¹^ORCID,Fang Liming²^ORCID,Chen Jing¹^ORCID,Wang Lina³^ORCID

Affiliation:

1. The Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China

2. Nanjing University of Aeronautics and Astronautics & Shenzhen Research Institute, Nanjing, China

3. The Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University & Zhengzhou Xinda Institute of Advanced Technology, Wuhan, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Wuhan Knowledge Innovation Program

Fundamental Research Funds for the Central Universities

Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness

Natural Science Foundation of Hubei Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612331

Reference56 articles.

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2. DNN Watermarking: Four Challenges and a Funeral

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5. Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell etal 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020). Tom B Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020).

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