Desensitizing for Improving Corruption Robustness in Point Cloud Classification through Adversarial Training

• URL: http://arxiv.org/abs/2509.23010v1
• Date: Sat, 27 Sep 2025 00:04:00 GMT
• Title: Desensitizing for Improving Corruption Robustness in Point Cloud Classification through Adversarial Training
• Authors: Zhiqiang Tian, Weigang Li, Chunhua Deng, Junwei Hu, Yongqiang Wang, Wenping Liu,
• Abstract summary: Over-reliance on input features is the root cause of DNN vulnerabilities.<n>We propose Desensitized Adsensitized Training' (DesenAT), generating adversarial samples using feature desensitization and conducting training within a self-distillation framework.<n>Extensive experiments on ModelNet-C and PointCloud-C demonstrate that the propose method can effectively improve the robustness of the model without reducing the performance of clean data sets.
• Score: 10.758400116136523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to scene complexity, sensor inaccuracies, and processing imprecision, point cloud corruption is inevitable. Over-reliance on input features is the root cause of DNN vulnerabilities. It remains unclear whether this issue exists in 3D tasks involving point clouds and whether reducing dependence on these features can enhance the model's robustness to corrupted point clouds. This study attempts to answer these questions. Specifically, we quantified the sensitivity of the DNN to point cloud features using Shapley values and found that models trained using traditional methods exhibited high sensitivity values for certain features. Furthermore, under an equal pruning ratio, prioritizing the pruning of highly sensitive features causes more severe damage to model performance than random pruning. We propose `Desensitized Adversarial Training' (DesenAT), generating adversarial samples using feature desensitization and conducting training within a self-distillation framework, which aims to alleviate DNN's over-reliance on point clouds features by smoothing sensitivity. First, data points with high contribution components are eliminated, and spatial transformation is used to simulate corruption scenes, generate adversarial samples, and conduct adversarial training on the model. Next, to compensate for information loss in adversarial samples, we use the self-distillation method to transfer knowledge from clean samples to adversarial samples, and perform adversarial training in a distillation manner.Extensive experiments on ModelNet-C and PointCloud-C demonstrate show that the propose method can effectively improve the robustness of the model without reducing the performance of clean data sets. This code is publicly available at \href{https://github.com/JerkyT/DesenAT/tree/master}{https://github.com/JerkyT/DesenAT}.

Related papers

• Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds [48.37843602248313]
  Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predictions.<n>We propose Consistency Model-based Adversarial Purification (CMAP), which optimize vectors within the latent space of a pre-trained consistency model to generate samples for restoring clean data.<n>CMAP significantly enhances robustness against strong adversarial attacks while preserving high natural accuracy.
  arXiv  Detail & Related papers  (2024-12-11T14:14:02Z)
• Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models [19.795578581043745]
  Test-time adaptation of 3D point clouds is crucial for mitigating discrepancies between training and testing samples in real-world scenarios. We introduce a novel 3D test-time adaptation method, termed 3DD-TTA, which stands for 3D Denoising Diffusion Test-Time Adaptation.
  arXiv  Detail & Related papers  (2024-11-21T00:04:38Z)
• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE enhances global feature representation of point cloud masked autoencoders by making them both discriminative and sensitive to transformations.<n>We propose a novel loss that explicitly penalizes invariant collapse, enabling the network to capture richer transformation cues while preserving discriminative representations.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• Training More Robust Classification Model via Discriminative Loss and Gaussian Noise Injection [7.535952418691443]
  We introduce a loss function applied at the penultimate layer that explicitly enforces intra-class compactness.<n>We also propose a class-wise feature alignment mechanism that brings noisy data clusters closer to their clean counterparts.<n>Our approach significantly reinforces model robustness to various perturbations while maintaining high accuracy on clean data.
  arXiv  Detail & Related papers  (2024-05-28T18:10:45Z)
• Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification [54.286437930350445]
  This paper highlights the challenges of point cloud classification posed by various forms of noise. We introduce an innovative point outlier cleansing method that harnesses the power of downstream classification models. Our proposed technique not only robustly filters diverse point cloud outliers but also consistently and significantly enhances existing robust methods for point cloud classification.
  arXiv  Detail & Related papers  (2023-07-20T13:47:30Z)
• Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning [54.51061298877896]
  We explore a more general denoising autoencoder for point cloud learning (Point-DAE) by investigating more types of corruptions beyond masking. Specifically, we degrade the point cloud with certain corruptions as input, and learn an encoder-decoder model to reconstruct the original point cloud from its corrupted version.
  arXiv  Detail & Related papers  (2022-11-13T08:02:03Z)
• PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition [111.55944556661626]
  We propose Point-Cloud Contrastive Adversarial Training (PointCAT) to boost the robustness of point cloud recognition models. We leverage a supervised contrastive loss to facilitate the alignment and uniformity of the hypersphere features extracted by the recognition model. To provide the more challenging corrupted point clouds, we adversarially train a noise generator along with the recognition model from the scratch.
  arXiv  Detail & Related papers  (2022-09-16T08:33:04Z)
• Can pruning improve certified robustness of neural networks? [106.03070538582222]
  We show that neural network pruning can improve empirical robustness of deep neural networks (NNs) Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models.
  arXiv  Detail & Related papers  (2022-06-15T05:48:51Z)
• Shape-invariant 3D Adversarial Point Clouds [111.72163188681807]
  Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been criticized for their noticeable point outliers. We propose a novel Point-Cloud Sensitivity Map to boost both the efficiency and imperceptibility of point perturbations.
  arXiv  Detail & Related papers  (2022-03-08T12:21:35Z)
• Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation [90.2445084743881]
  We present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance. Inspired by the recent contrastive loss in self-supervised tasks, we propose the guided point contrastive loss to enhance the feature representation and model generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T16:38:54Z)
• 3D Point Cloud Completion with Geometric-Aware Adversarial Augmentation [11.198650616143219]
  We show that training with adversarial samples can improve the performance of neural networks on 3D point cloud completion tasks. We propose a novel approach to generate adversarial samples that benefit both the performance of clean and adversarial samples. Experimental results show that training with the adversarial samples crafted by our method effectively enhances the performance of PCN on the ShapeNet dataset.
  arXiv  Detail & Related papers  (2021-09-21T13:16:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.