MultiNeRF: Multiple Watermark Embedding for Neural Radiance Fields

Yash Kulthe, Andrew Gilbert, John Collomosse

Track: long paper (up to 9 pages)

Keywords: NeRF, Watermarking, 3D, Copyright, Provenance

TL;DR: MultiNeRF embeds multiple watermarks within a learned NeRF representation for improved capacity without compromising visual quality in the rendered images.

Abstract:

We present MultiNeRF, a novel 3D watermarking method that enables embedding multiple uniquely keyed watermarks within images rendered by a single Neural Radiance Field (NeRF) model while maintaining high visual quality. Our approach extends the TensoRF NeRF model by incorporating a dedicated watermark grid alongside the existing geometry and appearance grids. This ensures higher watermark capacity without entangling watermark signals with scene content. We propose a FiLM-based conditional modulation mechanism that dynamically activates watermarks based on input identifiers, allowing multiple independent watermarks to be embedded and extracted without requiring model retraining. We validate MultiNeRF on the NeRF-Synthetic and LLFF datasets, demonstrating statistically significant improvements in robust capacity without compromising on rendering quality. By generalizing single-watermark NeRF methods into a flexible multi-watermarking framework, MultiNeRF provides a scalable solution for securing ownership and attribution in 3D content.

Presenter: John Collomosse

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Submission Number: 45