Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. This paper introduces a plug-and-play method to protect NeRF's copyright during its creation. We propose utilizing a pre-trained watermarking base model, enabling NeRF creators to embed binary messages directly while creating their NeRF. Our plug-and-play property ensures that NeRF creators can flexibly choose NeRF variants without excessive modifications. Leveraging our newly designed progressive distillation, we demonstrate performance on par with several leading-edge neural rendering methods.
Our plug-and-play method to watermark NeRF during its creation. (1) Building a watermarking base model: The watermarking base model \( \mathcal{F} \) can be sourced from a third party. We implement a HiDDeN framework to get the pre-trained message extractor as our watermarking base model \( \mathcal{F} \). During the training, the encoder \( \mathcal{E} \) encodes a randomly selected 48-kit message \( \mathbf{m} \) and cover image \( \mathbf{I}_o \) and outputs a watermarked image \( \mathbf{I}_{en} \). Then the message extractor \( \mathcal{F} \) extracts embedded message \( \hat{\mathbf{m}} \) from the watermarked image. Message encoder \( \mathcal{E} \) is discarded after building watermarking base model \( \mathcal{F} \). (2) NeRF creation with message distillation: NeRF creators first fix a copyright message \( \mathbf{m} \), then employ this base model \( \mathcal{F} \) to embed selected watermarks to NeRFs during the creation process via Progressive Global Rendering (PGR) and message distillation. When the optimization of NeRF is finalized, creators immediately obtain a watermarked NeRF. (3) Extracting watermark: Subsequently, they can utilize the base model \( \mathcal{F} \) to retrieve binary watermarks from the rendered images, asserting their ownership.