IPR-NeRF: Ownership Verification meets Neural Radiance Field (2401.09495v4)

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 not only does our approach maintain the fidelity (\ie, the rendering quality) of IPR-NeRF models, but it is also robust against both ambiguity and removal attacks compared to prior arts.

• The paper introduces a diffusion-based approach as the main contribution that significantly enhances the resilience of watermarks embedded in NeRF models.
• The method employs joint optimization for black-box and white-box protection, overcoming previous limitations by resisting noise and image degradation.
• The research underscores that robust watermark verification can be achieved without compromising image fidelity, establishing a foundation for future 3D model security.

Overview of Diffusion Models in NeRF Protection

Researchers have studied the robustness of watermark protection within Neural Radiance Field (NeRF) models and presented an innovative method using diffusion models. Black-box protection is critical, particularly given NeRF's rising commercial utilization for creating 3D scenes. Current techniques have struggled to extract watermarks after the rendering process or under noisy conditions. This work explores the potential of diffusion models to enhance watermark resilience against image degradations and noise, mitigating previous limitations.

Analysis of Watermarking Techniques and Noise Vulnerability

Previous methods like DeepStega and HiDDen directly watermark training images before model training, but suffer from a critical flaw where the watermark smooths out during rendering. Advanced approaches such as StegaNeRF are similarly ineffective against noise since their training did not accommodate noise as a potential form of attack. In their approach, the researchers propose using diffusion models, which inherently resist various forms of degradation attacks due to their objective of denoising, retaining watermark information effectively through the rendering process, and thus improving the integrity of NeRF model protection.

Technical Contributions and Dataset Influence

The paper's technical contributions lie in its successful application of diffusion models for both black-box and white-box NeRF protection. For black-box protection – a first in the field – the researchers showcase a joint optimization process for embedding and extracting watermarks in rendered scenes. The robustness of this methodology against noise provides a groundwork for future research in IPR strategies for NeRF. Concerning dataset influence, the researchers clarify that different datasets do not necessitate varying optimal thresholds for watermark quality in ownership verification, as the extracted watermark quality surpassed the necessary threshold across different scenes.

Fidelity Considerations and Future Directions

The fidelity of rendered images remains unhampered by the implementation of the proposed method, as depicted by direct comparisons with other methods in key performance metrics such as PSNR and SSIM. The paper concludes by emphasizing the importance of their contributions towards the field of NeRF IPR protection. Additionally, the adoption of normalization layers, a new method for white-box protection, invites future work to consider a dual approach for robust NeRF model security. The paper concludes with the researchers expressing their dedication to refining the manuscript and providing clear implementation details for reproducibility.

In essence, this paper provides a wider panorama of securing NeRF models through diffusion models, marking a significant step in the evolution of 3D data protection.