Pith. sign in

REVIEW 10 cited by

Adversarial Attack and Defense on Point Sets

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1902.10899 v4 pith:ZM3MAJY5 submitted 2019-02-28 cs.CV cs.AIcs.CRcs.LG

Jiancheng Yang , Qiang Zhang , Rongyao Fang , Bingbing Ni , Jinxian Liu , Qi Tian This is my paper · ORCID
classification cs.CVcs.AIcs.CRcs.LG
keywords pointattackcloudadversarialdatadefensecloudsproposed
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Emergence of the utility of 3D point cloud data in safety-critical vision tasks (e.g., ADAS) urges researchers to pay more attention to the robustness of 3D representations and deep networks. To this end, we develop an attack and defense scheme, dedicated to 3D point cloud data, for preventing 3D point clouds from manipulated as well as pursuing noise-tolerable 3D representation. A set of novel 3D point cloud attack operations are proposed via pointwise gradient perturbation and adversarial point attachment / detachment. We then develop a flexible perturbation-measurement scheme for 3D point cloud data to detect potential attack data or noisy sensing data. Notably, the proposed defense methods are even effective to detect the adversarial point clouds generated by a proof-of-concept attack directly targeting the defense. Transferability of adversarial attacks between several point cloud networks is addressed, and we propose an momentum-enhanced pointwise gradient to improve the attack transferability. We further analyze the transferability from adversarial point clouds to grid CNNs and the inverse. Extensive experimental results on common point cloud benchmarks demonstrate the validity of the proposed 3D attack and defense framework.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generating Adversarial Point Clouds Using Diffusion Model

    cs.CR 2025-07 conditional novelty 6.0 of 10

    A diffusion-model black-box attack generates adversarial 3D point clouds by conditioning reverse diffusion on other-class latent codes and constraining generation with density-aware Chamfer distance and MSE, reporting...

  2. Transferable and Undefendable Point Cloud Attacks via Medial Axis Transform

    cs.CV 2025-07 conditional novelty 6.0 of 10

    MAT-Adv generates adversarial point clouds by perturbing learned medial axis transform representations, improving transferability and undefendability over the tested baselines.

  3. Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking

    cs.CV 2025-07 conditional novelty 6.0 of 10

    ARLOT uses a least-squares graph over multi-agent bounding boxes to denoise adversarial detections and a two-stage Kalman tracking association to improve 3D multi-object tracking under point-cloud attacks.

  4. Cage-Based Deformation for Transferable and Undefendable Point Cloud Attack

    cs.CV 2025-07 conditional novelty 6.0 of 10

    CageAttack generates adversarial point clouds by perturbing cage vertices and propagating deformations via mean value coordinates, claiming a better trade-off between attack success, transferability, undefendability, ...

  5. Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field

    cs.CV 2024-12 conditional novelty 6.0 of 10

    Adversarial point cloud attacks become more imperceptible when perturbation directions are adjusted with a learned point-to-surface field that drags points back to the underlying surface.

  6. Adversarial shape perturbations on 3D point clouds

    cs.CV 2019-08 conditional novelty 6.0 of 10

    Three shape-deforming adversarial attacks on 3D point cloud classifiers maintain high success rates against point-removal defenses.

  7. Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles

    cs.CR 2026-07 conditional novelty 5.0 of 10

    GAN-based augmentation of poisoned 3D point cloud datasets amplifies attack effectiveness, increasing misclassification and operational impact on CAV decision-making by up to 3x compared to non-augmented baselines.

  8. KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor Search

    cs.CV 2025-06 conditional novelty 5.0 of 10

    KNN-Defense applies nearest-neighbor search in feature space to 3D point cloud classification, improving robustness to adversarial perturbations without retraining.

  9. Improving the Transferability of 3D Point Cloud Attack via Spectral-aware Admix and Optimization Designs

    cs.CV 2024-12 conditional novelty 5.0 of 10

    SAAO improves transferability of 3D point cloud adversarial attacks by performing Admix-style mixing in the graph Fourier domain with learnable weights and gradient-based path selection, yielding higher transfer attac...

  10. A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles

    cs.LG 2024-11 conditional novelty 2.0 of 10

    A literature survey of adversarial attacks and defenses for LiDAR-based perception in autonomous vehicles, with a gap analysis of current defenses.

Pith tools