NeRF Copyright Protection via Watermarked Color Representation
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Solution Overview
Problem
Current copyright protection methods for Neural Radiance Fields (NeRF) models are inadequate, as they either fail to protect the core model or result in visible distortions in rendered samples.
Innovation Solution
The proposed solution involves replacing the original color representation in NeRF models with a watermarked color representation and employing a distortion-resistant rendering scheme to ensure robust message extraction in 2D renderings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If copyright messages are embedded into rendered samples using off-the-shelf watermarking approaches, then the copyright of rendered samples is protected, but the core model remains unprotected and rendering quality deteriorates with visible distortions
Solution Approach 1:
The patent applies preliminary action by embedding copyright messages into the NeRF model weights during the model construction phase, rather than attempting to watermark rendered samples afterward. This ensures the core model itself is protected from the beginning, and the watermark is inherently integrated into the model's structure before any rendering occurs.
Solution Approach 2:
The patent uses an intermediary approach by introducing a specific watermark embedding mechanism that operates within the NeRF model's weight structure. This intermediary layer allows copyright protection to be integrated seamlessly into the model weights without directly interfering with the rendering process, thus maintaining rendering quality while ensuring protection.
2Measurement precision
If copyright messages are directly concatenated with input coordinates in NeRF, then bit accuracy improves, but visual distortion increases violating invisibility standard
Solution Approach 1:
The patent applies local quality by selectively modifying specific portions of the NeRF model weights where copyright messages are embedded, rather than uniformly affecting the entire model. This localized modification ensures that the watermark is integrated in a way that maintains overall rendering quality while achieving sufficient bit accuracy for copyright protection.
Solution Approach 2:
The patent uses parameter changes by adjusting the embedding strength and distribution of copyright messages within the model weights. By carefully controlling these parameters, the system achieves an optimal balance between bit accuracy for message extraction and visual quality for rendered outputs, avoiding excessive distortion.
3Object-affected harmful factors
If popular invisible watermarks on 2D images are used to create NeRF models, then invisibility is maintained, but robustness of message extraction deteriorates under distortions
Solution Approach 1:
The patent applies dimensionality change by transitioning from 2D image watermarking to 3D model weight watermarking. By embedding copyright messages in the NeRF model weights (a 3D implicit representation) rather than in 2D rendered images, the system achieves robustness against 2D distortions while maintaining invisibility, as the watermark survives the 3D-to-2D projection process.
Solution Approach 2:
The patent uses preliminary action by pre-embedding the copyright watermark into the NeRF model structure during training, so that the watermark is inherently part of the 3D representation. This preliminary embedding ensures that when the model is later rendered to 2D images, the watermark remains robust against various 2D distortions that may occur during transmission or processing.
Data Source
AI summary
A system for adding copyright protection to implicit 3D models is provided. The system includes a first MLP module, a second MLP module, a color feature encoder, a message feature encoder, and a feature fusion module. The first MLP module outputs a geometry parameter according to a 3D coordinate parameter obtained from a 3D model source. The second MLP module outputs a base-colors parameter according to a viewing-directions parameter obtained from the 3D model source and outcomes of the first MLP module. The color feature encoder concatenates the geometry parameter, the viewing-directions parameter, and the base-colors parameter to obtain a spatial descriptor and transforms the spatial descriptor to a high-dimensional color feature field. The message feature encoder maps messages to higher dimensions so as to obtain a message feature field. The feature fusion module generates a watermarked color representation and embed the watermarked color representation into the 3D model source.