WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights

Youngdong Jang, Dong In Lee, MinHyuk Jang, Jong Wook Kim, Feng Yang, Sangpil Kim

Abstract:

The advances in the Neural Radiance Fields (NeRF) research offer extensive applications in diverse domains, but
protecting their copyrights has not yet been researched in
depth. Recently, NeRF watermarking has been considered
one of the pivotal solutions for safely deploying NeRFbased 3D representations. However, existing methods are
designed to apply only to implicit or explicit NeRF representations. In this work, we introduce an innovative watermarking method that can be employed in both representations of NeRF. This is achieved by fine-tuning NeRF to
embed binary messages in the rendering process. In detail,
we propose utilizing the discrete wavelet transform in the
NeRF space for watermarking. Furthermore, we adopt a deferred back-propagation technique and introduce a combination with the patch-wise loss to improve rendering quality and bit accuracy with minimum trade-offs. We evaluate our method in three different aspects: capacity, invisibility, and robustness of the embedded watermarks in the
2D-rendered images. Our method achieves state-of-the-art
performance with faster training speed over the compared
state-of-the-art methods. Project page: https://kuailab.github.io/cvpr2024waterf/