Segments-aware universal adversarial perturbations purification on 3D point cloud classifiers
Introduction3D point cloud classifiers, while powerful for representing real-world objects and environments, are vulnerable to adversarial perturbations, particularly Universal Adversarial Perturbations (UAPs). These UAPs pose significant security threats due to their input-agnostic nature. Current...
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| Автори: | , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Frontiers Media S.A.
2025-11-01
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| Серія: | Frontiers in Computer Science |
| Предмети: | |
| Онлайн доступ: | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1626359/full |
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