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MarkNeRF: Watermarking for Neural Radiance Field

Lifeng Chen1,2, Jia Liu1,2,*, Wenquan Sun1,2, Weina Dong1,2, Xiaozhong Pan1,2

1 Cryptographic Engineering Department, Institute of Cryptographic Engineering, Engineering University of PAP, Xi’an, 710086, China
2 Key Laboratory of Network and Information Security of PAP, Xi’an, 710086, China

* Corresponding Author: Jia Liu. Email: email

(This article belongs to the Special Issue: Multimedia Security in Deep Learning)

Computers, Materials & Continua 2024, 80(1), 1235-1250. https://doi.org/10.32604/cmc.2024.051608

Abstract

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field (NeRF) models. We employ an embedding network to integrate the watermark into the images within the training set. Then, the NeRF model is utilized for 3D modeling. For copyright verification, a secret image is generated by inputting a confidential viewpoint into NeRF. On this basis, design an extraction network to extract embedded watermark images from confidential viewpoints. In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario, the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s copyright. The experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks, thereby substantiating the effectiveness of our approach in safeguarding NeRF.

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Cite This Article

APA Style
Chen, L., Liu, J., Sun, W., Dong, W., Pan, X. (2024). Marknerf: watermarking for neural radiance field. Computers, Materials & Continua, 80(1), 1235-1250. https://doi.org/10.32604/cmc.2024.051608
Vancouver Style
Chen L, Liu J, Sun W, Dong W, Pan X. Marknerf: watermarking for neural radiance field. Comput Mater Contin. 2024;80(1):1235-1250 https://doi.org/10.32604/cmc.2024.051608
IEEE Style
L. Chen, J. Liu, W. Sun, W. Dong, and X. Pan, “MarkNeRF: Watermarking for Neural Radiance Field,” Comput. Mater. Contin., vol. 80, no. 1, pp. 1235-1250, 2024. https://doi.org/10.32604/cmc.2024.051608



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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