Shielding the Unseen: Privacy Protection through Poisoning NeRF with
  Spatial Deformation
Yihan Wu Brandon Y. Feng Heng Huang
In this paper, we introduce an innovative method of safeguarding user privacy
against the generative capabilities of Neural Radiance Fields (NeRF) models.
Our novel poisoning attack method induces changes to observed views that are
imperceptible to the human eye, yet potent enough to disrupt NeRF's ability to
accurately reconstruct a 3D scene. To achieve this, we devise a bi-level
optimization algorithm incorporating a Projected Gradient Descent (PGD)-based
spatial deformation. We extensively test our approach on two common NeRF
benchmark datasets consisting of 29 real-world scenes with high-quality images.
Our results compellingly demonstrate that our privacy-preserving method
significantly impairs NeRF's performance across these benchmark datasets.
Additionally, we show that our method is adaptable and versatile, functioning
across various perturbation strengths and NeRF architectures. This work offers
valuable insights into NeRF's vulnerabilities and emphasizes the need to
account for such potential privacy risks when developing robust 3D scene
reconstruction algorithms. Our study contributes to the larger conversation
surrounding responsible AI and generative machine learning, aiming to protect
user privacy and respect creative ownership in the digital age.