$S^2$NeRF: Privacy-preserving Training Framework for NeRF
<|reference_start|>$S^2$NeRF: Privacy-preserving Training Framework for NeRF: Neural Radiance Fields (NeRF) have revolutionized 3D computer vision and graphics, facilitating novel view synthesis and influencing sectors like extended reality and e-commerce. However, NeRF's dependence on extensive data collection, including sensitive scene image data, introduces significant privacy risks when users upload this data for model training. To address this concern, we first propose SplitNeRF, a training framework that incorporates split learning (SL) techniques to enable privacy-preserving collaborative model training between clients and servers without sharing local data. Despite its benefits, we identify vulnerabilities in SplitNeRF by developing two attack methods, Surrogate Model Attack and Scene-aided Surrogate Model Attack, which exploit the shared gradient data and a few leaked scene images to reconstruct private scene information. To counter these threats, we introduce $S^2$NeRF, secure SplitNeRF that integrates effective defense mechanisms. By introducing decaying noise related to the gradient norm into the shared gradient information, $S^2$NeRF preserves privacy while maintaining a high utility of the NeRF model. Our extensive evaluations across multiple datasets demonstrate the effectiveness of $S^2$NeRF against privacy breaches, confirming its viability for secure NeRF training in sensitive applications.<|reference_end|>
@article{zhang2024$s^2$nerf:,
    title={$S^2$NeRF: Privacy-preserving Training Framework for NeRF},
    author={Bokang Zhang, Yanglin Zhang, Zhikun Zhang, Jinglan Yang, Lingying
  Huang, Junfeng Wu},
    journal={arXiv preprint arXiv:2409.01661},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01661},
    primaryClass={cs.CR cs.CV cs.LG}
}