Securing IP in edge AI: neural network watermarking for multimodal models-Reference-Cited by-同舟云学术

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Securing IP in edge AI: neural network watermarking for multimodal models

Published:2024-08-16 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Nie Hewang,Lu Songfeng

Funder

the National Key R&D Program of China

the Major Research Plan of Hubei Provience

Key Research & Development Plan of Hubei Province of China

the project of Science,Technology and Innovation Commission of Shenzhen Municipality of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-024-05746-x.pdf

Reference53 articles.

1. Ren L, Jia Z, Laili Y, Huang D (2023) Deep learning for time-series prediction in iiot: progress, challenges, and prospects. IEEE Trans Neural Netw Learn Syst

2. Rueden L, Mayer S, Beckh K, Georgiev B, Giesselbach S, Heese R, Kirsch B, Pfrommer J, Pick A, Ramamurthy R, Walczak M, Garcke J, Bauckhage C, Schuecker J (2023) Informed machine learning - a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans Knowl Data Eng 35(1):614–633. https://doi.org/10.1109/TKDE.2021.3079836

3. Ahmed I, Jeon G, Piccialli F (2022) From artificial intelligence to explainable artificial intelligence in industry 4.0: A survey on what, how, and where. IEEE Trans Ind Inform 18(8):5031–5042. https://doi.org/10.1109/TII.2022.3146552

4. Chen J, Xue J, Wang Y, Huang L, Baker T, Zhou Z (2023) Privacy-preserving and traceable federated learning for data sharing in industrial iot applications. Expert Syst Appl 213:119036. https://doi.org/10.1016/j.eswa.2022.119036

5. Zhang T, Gao L, He C, Zhang M, Krishnamachari B, Avestimehr AS (2022) Federated learning for the internet of things: Applications, challenges, and opportunities. IEEE Internet Things Mag 5(1):24–29. https://doi.org/10.1109/IOTM.004.2100182

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