ZeroW-NeRF: Uncertainty-driven Zero-watermark Copyright Protection for Neural Radiance Fields
Authors: Zhu, X., Wang, Z., You, L., Yang, X., Sheng, Z., Gu, Y., Zhao, K., Zeng, D.
Journal: IEEE Transactions on Multimedia
Publication Date: 01/01/2026
eISSN: 1941-0077
ISSN: 1520-9210
DOI: 10.1109/TMM.2026.3688338
Abstract:Current research on watermarking for Neural Radiance Fields (NeRF) primarily focuses on embedding watermarks into rendered images or achieving copyright protection by fine-tuning model parameters. However, directly embedding watermarks into NeRF parameters inevitably alters the original parameter distribution, leading to a degradation in rendering quality. Moreover, existing methods fail to fully exploit the rich 3D information inherent in the radiance field. To address these issues, we propose ZeroW-NeRF, a novel zero-watermark approach for NeRF. Instead of altering model parameters, ZeroW-NeRF encodes scene information via uncertainty at each sampling point, seamlessly integrating the watermark into the rendering pipeline without compromising the integrity of the original model. Leveraging a self-attention mechanism, our approach captures long-range dependencies within the radiance field, enabling holistic scene representation. Consequently, ZeroW-NeRF achieves lossless copyright protection while preserving rendering quality, offering a robust watermarking scheme inherently aligned with the rendering process. We perform extensive evaluations of ZeroW-NeRF on standard NeRF datasets and popular NeRF variants. Experimental results demonstrate that ZeroW-NeRF effectively enforces copyright protection without compromising rendering efficiency or visual quality. Additionally, it exhibits superior performance in terms of watermark capacity and robustness, positioning it as a highly efficient and robust solution for safeguarding the intellectual property of NeRF-based systems.
Source: Scopus