MarkNerf:Watermarking for Neural Radiance Field

TL;DR

This paper introduces MarkNerf, a watermarking method for neural radiance fields that embeds and extracts watermarks to protect 3D model copyrights, showing robustness against noise attacks.

Contribution

The paper presents a novel watermarking algorithm for NeRF models that enables copyright verification through backdoor image generation and hyperparameterization-based watermark extraction.

Findings

Effective copyright protection demonstrated

Watermarks maintain visual quality

Robust against noise attacks

Abstract

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.