One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation
<|reference_start|>One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation: 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.<|reference_end|>
@article{ergezer2024one,
    title={One Noise to Rule Them All: Multi-View Adversarial Attacks with
  Universal Perturbation},
    author={Mehmet Ergezer and Phat Duong and Christian Green and Tommy Nguyen and
  Abdurrahman Zeybey},
    journal={2nd International Conference on Artificial Intelligence and
  Applications (ICAIA 2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02287},
    primaryClass={cs.CV cs.AI}
}