Open Access

ARTICLE

MarkNeRF: Watermarking for Neural Radiance Field

Lifeng Chen^1,2, Jia Liu^1,2,*, Wenquan Sun^1,2, Weina Dong^1,2, Xiaozhong Pan^1,2

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:

(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

Received 10 March 2024; Accepted 01 June 2024; Issue published 18 July 2024

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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Keywords

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