  This paper presents a novel universal perturbation method for generating
robust multi-view adversarial examples in 3D object recognition. Unlike
conventional attacks limited to single views, our approach operates on multiple
2D images, offering a practical and scalable solution for enhancing model
scalability and robustness. This generalizable method bridges the gap between
2D perturbations and 3D-like attack capabilities, making it suitable for
real-world applications.
  Existing adversarial attacks may become ineffective when images undergo
transformations like changes in lighting, camera position, or natural
deformations. We address this challenge by crafting a single universal noise
perturbation applicable to various object views. Experiments on diverse
rendered 3D objects demonstrate the effectiveness of our approach. The
universal perturbation successfully identified a single adversarial noise for
each given set of 3D object renders from multiple poses and viewpoints.
Compared to single-view attacks, our universal attacks lower classification
confidence across multiple viewing angles, especially at low noise levels. A
sample implementation is made available at
https://github.com/memoatwit/UniversalPerturbation.
