Targeted Adversarial Attacks on Generalizable Neural Radiance Fields

Note: We don't have the ability to review paper

PubDate: Oct 2023

Teams: Peter Pazmany Catholic University;Nokia Bell Labs

Writers: Andras Horvath, Csaba M. Jozsa

PDF: Targeted Adversarial Attacks on Generalizable Neural Radiance Fields

Abstract

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.

You may also like...

Paper