mlsanityData to 13 Oct 2026

Minimal Adversarial Examples for Deep Learning on 3D Point Clouds

arXiv 2008.12066 ↗First posted 27 Aug 2020cs.CVcited by 41

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Abstract (arXiv)

With recent developments of convolutional neural networks, deep learning for 3D point clouds has shown significant progress in various 3D scene understanding tasks, e.g., object recognition, semantic segmentation. In a safety-critical environment, it is however not well understood how such deep learning models are vulnerable to adversarial examples. In this work, we explore adversarial attacks for point cloud-based neural networks. We propose a unified formulation for adversarial point cloud generation that can generalise two different attack strategies. Our method generates adversarial examples by attacking the classification ability of point cloud-based networks while considering the perceptibility of the examples and ensuring the minimal level of point manipulations. Experimental results show that our method achieves the state-of-the-art performance with higher than 89% and 90% of attack success rate on synthetic and real-world data respectively, while manipulating only about 4% of the total points.

Key facts. Cited by 41 papers in the map (data through 13 Oct 2026). First posted on arXiv 27 Aug 2020.

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Citing papers per year: 2021: 5; 2022: 8; 2023: 9; 2024: 9; 2025: 7; 2026: 3.

41 papers.

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  1. 2021PointGuard: Provably Robust 3D Point Cloud Classificationcited by 49
  2. 2022Isometric 3D Adversarial Examples in the Physical Worldcited by 25
  3. 2023A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attackscited by 19
  4. 2022Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradientscited by 17
  5. 2022OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Datacited by 13
  6. 2022LPF-Defense: 3D Adversarial Defense based on Frequency Analysiscited by 10
  7. 20223DeformRS: Certifying Spatial Deformations on Point Cloudscited by 10
  8. 2022PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examplescited by 9
  9. 20233DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attackcited by 9
  10. 2022Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusioncited by 8
  11. 2021Local Aggressive Adversarial Attacks on 3D Point Cloudcited by 7
  12. 2021Explainability-Aware One Point Attack for Point Cloud Neural Networkscited by 7
29 more
  1. 2022Invariance-Aware Randomized Smoothing Certificatescited by 7
  2. 2021Adversarial Attack by Limited Point Cloud Surface Modificationscited by 6
  3. 2023PointCert: Point Cloud Classification with Deterministic Certified Robustness Guaranteescited by 6
  4. 2023Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Surveycited by 5
  5. 2023Refutation of Shapley Values for XAI -- Additional Evidencecited by 4
  6. 2024Towards Robust 3D Pose Transfer with Adversarial Learningcited by 4
  7. 2021On Adversarial Robustness of Point Cloud Semantic Segmentationcited by 3
  8. 2023Robustifying Point Cloud Networks by Refocusingcited by 2
  9. 2024Robust 3D Point Clouds Classification based on Declarative Defenderscited by 2
  10. 2025NoPain: No-box Point Cloud Attack via Optimal Transport Singular Boundarycited by 2
  11. 2025InfoCons: Identifying Interpretable Critical Concepts in Point Clouds via Information Theorycited by 2
  12. 2023Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Networkcited by 1
  13. 2023SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectorscited by 1
  14. 2024Flow AM: Generating Point Cloud Global Explanations by Latent Alignmentcited by 1
  15. 2024Eidos: Efficient, Imperceptible Adversarial 3D Point Cloudscited by 1
  16. 2024Adversarial 3D Virtual Patches using Integrated Gradientscited by 1
  17. 2024LiDAttack: Robust Black-box Attack on LiDAR-based Object Detectioncited by 1
  18. 2025Generating Adversarial Point Clouds Using Diffusion Modelcited by 1
  19. 2023Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Realitycited by 0
  20. 2024Adversarial Attacks on LiDAR-Based Tracking Across Road Users: Robustness Evaluation and Target-Aware Black-Box Methodcited by 0
  21. 2024Hard-Label Black-Box Attacks on 3D Point Cloudscited by 0
  22. 2024Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Fieldcited by 0
  23. 2025Hi-ALPS -- An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Drivingcited by 0
  24. 2025Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attackscited by 0
  25. 2025Cage-Based Deformation for Transferable and Undefendable Point Cloud Attackcited by 0
  26. 20253D-ANC: Adaptive Neural Collapse for Robust 3D Point Cloud Recognitioncited by 0
  27. 2026APC: Transferable and Efficient Adversarial Point Counterattack for Robust 3D Point Cloud Recognitioncited by 0
  28. 2026Beyond Defenses: Manifold-Aligned Regularization for Intrinsic 3D Point Cloud Robustnesscited by 0
  29. 2026Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectorscited by 0