Targeted Adversarial Attacks on Generalizable Neural Radiance Fields
Andras Horvath Csaba M. Jozsa
Neural Radiance Fields (NeRFs) have recently emerged as a powerful tool for
3D scene representation and rendering. These data-driven models can learn to
synthesize high-quality images from sparse 2D observations, enabling realistic
and interactive scene reconstructions. However, the growing usage of NeRFs in
critical applications such as augmented reality, robotics, and virtual
environments could be threatened by adversarial attacks.
  In this paper we present how generalizable NeRFs can be attacked by both
low-intensity adversarial attacks and adversarial patches, where the later
could be robust enough to be used in real world applications. We also
demonstrate targeted attacks, where a specific, predefined output scene is
generated by these attack with success.