KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor Search
Deep neural networks (DNNs) have demonstrated remarkable performance in analyzing 3D point cloud data. However, their vulnerability to adversarial attacks-such as point dropping, shifting, and adding-poses a critical challenge to the reliability of 3D vision systems. These attacks can compromise the semantic and structural integrity of point clouds, rendering many existing defense mechanisms ineffective. To address this issue, a defense strategy named KNN-Defense is proposed, grounded in the manifold assumption and nearest-neighbor search in feature space. Instead of reconstructing surface geometry or enforcing uniform point distributions, the method restores perturbed inputs by leveraging the semantic similarity of neighboring samples from the training set. KNN-Defense is lightweight and computationally efficient, enabling fast inference and making it suitable for real-time and practical applications. Empirical results on the ModelNet40 dataset demonstrated that KNN-Defense significantly improves robustness across various attack types. In particular, under point-dropping attacks-where many existing methods underperform due to the targeted removal of critical points-the proposed method achieves accuracy gains of 20.1%, 3.6%, 3.44%, and 7.74% on PointNet, PointNet++, DGCNN, and PCT, respectively. These findings suggest that KNN-Defense offers a scalable and effective solution for enhancing the adversarial resilience of 3D point cloud classifiers. (An open-source implementation of the method, including code and data, is available at https://github.com/nimajam41/3d-knn-defense).
Code (1)
Tasks
Semantic SimilaritySemantic Textual SimilarityMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Defense-PointNet: Protecting PointNet Against Adversarial Attacks
Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks. Many works focus on adversarial attacks and defenses on 2D images, but few focus on 3…
Adversarial RobustnessProvable Defense Against Clustering Attacks on 3D Point Clouds
Lately, the literature on adversarial robustness spans from images to other domains such as point clouds. In this work, we consider clustering attacks on 3D point clouds and devise a provable defense mechanism to counter…
Adversarial RobustnessClusteringIF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
Point cloud is an important 3D data representation widely used in many essential applications. Leveraging deep neural networks, recent works have shown great success in processing 3D point clouds. However, those deep neu…
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
3D object classification and segmentation using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust ag…
3D Object ClassificationGeneral Classificationimage-classificationImage ClassificationDUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose a Denoiser and UPsampler Network (DUP-Net) structure as defenses for 3D adversar…
DenoisingPoint Cloud Classification