IPR-NeRF: Ownership Verification meets Neural Radiance Field
Win Kent Ong, Kam Woh Ng, Chee Seng Chan, Yi Zhe Song, Tao Xiang
TL;DR
This paper introduces IPR-NeRF, a comprehensive framework for protecting NeRF models' intellectual property through watermarking and digital signatures, ensuring ownership verification without compromising model quality.
Contribution
It presents novel black-box and white-box IP protection methods for NeRF models, including diffusion-based watermarking and embedded digital signatures, with robustness against attacks.
Findings
Maintains high rendering fidelity
Robust against ambiguity attacks
Effective against removal attacks
Abstract
Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community in the recent past with state-of-the-art visual quality and produced impressive demonstrations. Since then, technopreneurs have sought to leverage NeRF models into a profitable business. Therefore, NeRF models make it worth the risk of plagiarizers illegally copying, re-distributing, or misusing those models. This paper proposes a comprehensive intellectual property (IP) protection framework for the NeRF model in both black-box and white-box settings, namely IPR-NeRF. In the black-box setting, a diffusion-based solution is introduced to embed and extract the watermark via a two-stage optimization process. In the white-box setting, a designated digital signature is embedded into the weights of the NeRF model by adopting the sign loss objective. Our extensive experiments demonstrate that…
Peer Reviews
Decision·Submitted to ICLR 2024
- I think the paper investigates an important and interesting topic in the 3D vision community. - The paper is well-written and easy to follow. - The comprehensive experimental results significantly demonstrate the benefit of the proposed method.
- The motivation for using the diffusion model to learn black-box protection is unclear. It would be great if the authors could provide more elaboration.
1. Well-motivated: Since NeRF-based 3D reconstruction is more and more easy-to-use, people are sharing their NeRF models on web. Thus, it is worth exploring how to alleviate copying, re-distributing, or misusing those models. 2. Impressive Results: The baseline works can protect NeRF models but degrade the rendering quality obviously. This work nearly does not affect the rendering quality. 3. Easy-to-follow draft: the draft is well-written and the figures are easy-to-understand.
1. Scalability to explicit NeRF representations: To accelerate NeRF inference and rendering, multiple works [1,2,3] have proposed to use explicit representations (e.g., grid, mesh, and point cloud) instead of MLP as the NeRF representations. Modifying the weights of explicit NeRF representations seems to have a larger effect on the rendering quality as compared to implicit representations because it can be regarded as changing the location/color of the grid/mesh/point cloud. Thus, it is not sure
1. To the best of my knowledge, It first proposes to embed both watermarking and textual signature simultaneously. 2. Using diffusion models seems to be quite effective compared to the previously suggested simple decoder-based methods. 3. Various experiments to show the robustness of the proposed scheme make the paper more convincing.
1. My major concern is that It lacks the original technical contribution. It is interesting to see that the diffusion models (DDIM) are quite effective in this task. However, I think it may not be a sufficient contribution to be a full conference paper. 2. For textual embedding, the authors adopted the technique from DeepIPR work into neural fields. It is also useful information to the community, but I still think the direct application to the well-defined neural network architecture would not
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Adversarial Robustness in Machine Learning · Advanced Memory and Neural Computing