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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Bibliografische Detailangaben

Hauptverfasser:	Yang Gao, Xianrui Chang, Haoran Li, Jian Xu
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Frontiers Media S.A. 2025-11-01
Schriftenreihe:	Frontiers in Computer Science
Schlagworte:	deep learning computer vision 3D point cloud adversarial attack security and privacy defense
Online-Zugang:	https://www.frontiersin.org/articles/10.3389/fcomp.2025.1626359/full
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