Abstract
Remarkable advancements in the recolorization of Neural Radiance Fields (NeRF) have simplified the process of modifying NeRF's color attributes. Yet, with the potential of NeRF to serve as shareable digital assets, there's a concern that malicious users might alter the color of NeRF models and falsely claim the recolorized version as their own. To safeguard against such breaches of ownership, enabling original NeRF creators to establish rights over recolorized NeRF is crucial. While ap- proaches like CopyRNeRF have been introduced to embed binary mes- sages into NeRF models as digital signatures for copyright protection, the process of recolorization can remove these binary messages. In our paper, we present GeometrySticker, a method for seamlessly integrating binary messages into the geometry components of radiance fields, akin to applying a sticker. GeometrySticker can embed binary messages into NeRF models while preserving the effectiveness of these messages against recolorization. Our comprehensive studies demonstrate that Geometry- Sticker is adaptable to prevalent NeRF architectures and maintains a commendable level of robustness against various distortions.
Framework
The framework of our proposed GeometrySticker. We employ a Multilayer Perceptron (MLP) to convert binary messages into a format that aligns with NeRF geometry representation. Following this, a learnable Cumulative Distribution Function (CDF) is utilized to select 3D points close to the object surfaces with high geometry values as the cover medium. Subsequently, an addition is applied to attach the message (Equation (4)) onto the chosen cover media. Through this message attachment process, a watermarked NeRF is generated, capable of retaining its efficacy across diverse recolorizations. If any unauthorized changes to color attributes occur, NeRF owners can retrieve the integrated watermarks to assert their ownership.
