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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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Principais autores: Yang Gao, Xianrui Chang, Haoran Li, Jian Xu
Format: Artigo
Sprog:Inglês
Udgivet: Frontiers Media S.A. 2025-11-01
Serier:Frontiers in Computer Science
Fag:
deep learning
computer vision
3D point cloud
adversarial attack
security and privacy
defense
Online adgang:https://www.frontiersin.org/articles/10.3389/fcomp.2025.1626359/full
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