Protecting Neural Radiance Field (NeRF) Models: The IPR-NeRF Framework

Neural Radiance Field (NeRF) models have gained significant attention in the computer vision community due to their state-of-the-art visual quality and impressive demonstrations. These models have the potential to be highly profitable in business applications, leading to concerns about plagiarism and misuse. To address these issues, this paper introduces a comprehensive intellectual property (IP) protection framework called IPR-NeRF.

Black-Box Setting: Diffusion-based Watermarking

In the black-box setting, where the internal structure of the NeRF model is not known, the IPR-NeRF framework proposes a diffusion-based solution for embedding and extracting watermarks. This process involves a two-stage optimization process, ensuring that the watermark is embedded in the model without compromising its visual quality. The diffusion-based approach provides robustness against removal or modification attacks on the watermark.

White-Box Setting: Digital Signature Embedding

In the white-box setting, where the NeRF model’s internal weights are accessible, the IPR-NeRF framework adopts a designated digital signature embedded into the weights of the model using the sign loss objective. By incorporating a digital signature directly into the model, this method ensures that any attempts to copy or redistribute the model will be traceable back to its source. Additionally, the sign loss objective enhances robustness against attacks that attempt to remove or alter the signature.

The IPR-NeRF framework has undergone extensive experiments to evaluate its effectiveness. The results demonstrate that this approach not only maintains the fidelity of NeRF models, preserving their rendering quality, but also proves robust against both ambiguity and removal attacks compared to previous methods.

In conclusion, with the growing interest in NeRF models and their potential for commercial applications, protecting intellectual property rights becomes crucial. The IPR-NeRF framework provides a comprehensive solution for safeguarding NeRF models from plagiarism, illegal copying, and unauthorized use. Its effectiveness in maintaining visual quality and robustness against attacks make it a valuable tool for technopreneurs looking to leverage NeRF models profitably while ensuring protection against misuse.
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