# MarkNerf:Watermarking for Neural Radiance Field

arXiv: https://arxiv.org/abs/2309.11747 · HTML: https://mlsanity.com/paper/2309.11747/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2023.

## Built on by (most cited first, 4 of 4)

- [GS-Hider: Hiding Messages into 3D Gaussian Splatting](https://mlsanity.com/paper/2405.15118/) (2024)
- [MI-NeRF: Learning a Single Face NeRF from Multiple Identities](https://mlsanity.com/paper/2403.19920/) (2024)
- [Proactive Schemes: A Survey of Adversarial Attacks for Social Good](https://mlsanity.com/paper/2409.16491/) (2024)
- [NGS-Marker: Robust Native Watermarking for 3D Gaussian Splatting](https://mlsanity.com/paper/2608.17447/) (2026)

## Abstract (arXiv)

A watermarking algorithm is proposed in this paper to address the copyright protection issue of implicit 3D models. The algorithm involves embedding watermarks into the images in the training set through an embedding network, and subsequently utilizing the NeRF model for 3D modeling. A copyright verifier is employed to generate a backdoor image by providing a secret perspective as input to the neural radiation field. Subsequently, a watermark extractor is devised using the hyperparameterization method of the neural network to extract the embedded watermark image from that perspective. In a black box scenario, if there is a suspicion that the 3D model has been used without authorization, the verifier can extract watermarks from a secret perspective to verify network copyright. Experimental results demonstrate that the proposed algorithm effectively safeguards the copyright of 3D models. Furthermore, the extracted watermarks exhibit favorable visual effects and demonstrate robust resistance against various types of noise attacks.