CopyRNeRF：保護神經輝度場的版權
CopyRNeRF: Protecting the CopyRight of Neural Radiance Fields
July 21, 2023
作者: Ziyuan Luo, Qing Guo, Ka Chun Cheung, Simon See, Renjie Wan
cs.AI
摘要
神經輻射場（Neural Radiance Fields，NeRF）有潛力成為媒體的重要表現形式。由於訓練 NeRF 從未是一項輕鬆的任務，保護其模型版權應該是一項優先考量。本文通過分析可能的版權保護解決方案的優缺點，提出通過將 NeRF 中的原始顏色表示形式替換為帶水印的顏色表示形式來保護 NeRF 模型的版權。然後，設計了一種抗失真渲染方案，以確保在 NeRF 的 2D 渲染中能夠抽取出強韌的訊息。我們提出的方法可以直接保護 NeRF 模型的版權，同時在與其他可選解決方案相比時保持高渲染質量和位準確性。
English
Neural Radiance Fields (NeRF) have the potential to be a major representation
of media. Since training a NeRF has never been an easy task, the protection of
its model copyright should be a priority. In this paper, by analyzing the pros
and cons of possible copyright protection solutions, we propose to protect the
copyright of NeRF models by replacing the original color representation in NeRF
with a watermarked color representation. Then, a distortion-resistant rendering
scheme is designed to guarantee robust message extraction in 2D renderings of
NeRF. Our proposed method can directly protect the copyright of NeRF models
while maintaining high rendering quality and bit accuracy when compared among
optional solutions.