CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction

• URL: http://arxiv.org/abs/2404.14042v1
• Date: Mon, 22 Apr 2024 09:55:50 GMT
• Title: CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction
• Authors: Wenhao Lan, Yijun Yang, Haihua Shen, Shan Li,
• Abstract summary: We propose CloudFort, a novel defense mechanism designed to enhance the robustness of 3D point cloud classifiers against backdoor attacks. Our results show that CloudFort significantly enhances the security of 3D point cloud classification models without compromising their accuracy on benign samples.
• Score: 4.481857838188627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing adoption of 3D point cloud data in various applications, such as autonomous vehicles, robotics, and virtual reality, has brought about significant advancements in object recognition and scene understanding. However, this progress is accompanied by new security challenges, particularly in the form of backdoor attacks. These attacks involve inserting malicious information into the training data of machine learning models, potentially compromising the model's behavior. In this paper, we propose CloudFort, a novel defense mechanism designed to enhance the robustness of 3D point cloud classifiers against backdoor attacks. CloudFort leverages spatial partitioning and ensemble prediction techniques to effectively mitigate the impact of backdoor triggers while preserving the model's performance on clean data. We evaluate the effectiveness of CloudFort through extensive experiments, demonstrating its strong resilience against the Point Cloud Backdoor Attack (PCBA). Our results show that CloudFort significantly enhances the security of 3D point cloud classification models without compromising their accuracy on benign samples. Furthermore, we explore the limitations of CloudFort and discuss potential avenues for future research in the field of 3D point cloud security. The proposed defense mechanism represents a significant step towards ensuring the trustworthiness and reliability of point-cloud-based systems in real-world applications.

Related papers

• Transferable 3D Adversarial Shape Completion using Diffusion Models [8.323647730916635]
  3D point cloud feature learning has significantly improved the performance of 3D deep-learning models. Existing attack methods primarily focus on white-box scenarios and struggle to transfer to recently proposed 3D deep-learning models. In this paper, we generate high-quality adversarial point clouds using diffusion models. Our proposed attacks outperform state-of-the-art adversarial attack methods against both black-box models and defenses.
  arXiv  Detail & Related papers  (2024-07-14T04:51:32Z)
• iBA: Backdoor Attack on 3D Point Cloud via Reconstructing Itself [5.007492246056274]
  MirrorAttack is a novel effective 3D backdoor attack method. It implants the trigger by simply reconstructing a clean point cloud with an auto-encoder. We achieve state-of-the-art ASR on different types of victim models with the intervention of defensive techniques.
  arXiv  Detail & Related papers  (2024-03-09T09:15:37Z)
• 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)
• Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion [70.60038549155485]
  Deep 3D point cloud models are sensitive to adversarial attacks, which poses threats to safety-critical applications such as autonomous driving. This paper introduces a novel distortion-aware defense framework that can rebuild the pristine data distribution with a tailored intensity estimator and a diffusion model.
  arXiv  Detail & Related papers  (2022-11-29T14:32:43Z)
• PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples [63.84378007819262]
  We propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones. We show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01.
  arXiv  Detail & Related papers  (2022-11-22T14:15:41Z)
• PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition [29.840946461846]
  3D Point cloud is a critical data representation in many real-world applications like autonomous driving, robotics, and medical imaging. Deep learning is notorious for its vulnerability to adversarial attacks. We propose PointDP, a purification strategy that leverages diffusion models to defend against 3D adversarial attacks.
  arXiv  Detail & Related papers  (2022-08-21T04:49:17Z)
• PointAttN: You Only Need Attention for Point Cloud Completion [89.88766317412052]
  Point cloud completion refers to completing 3D shapes from partial 3D point clouds. We propose a novel neural network for processing point cloud in a per-point manner to eliminate kNNs. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes.
  arXiv  Detail & Related papers  (2022-03-16T09:20:01Z)
• Boosting 3D Adversarial Attacks with Attacking On Frequency [6.577812580043734]
  We propose a novel point cloud attack (dubbed AOF) that pays more attention on the low-frequency component of point clouds. Experiments validate that AOF can improve the transferability significantly compared to state-of-the-art (SOTA) attacks.
  arXiv  Detail & Related papers  (2022-01-26T13:52:17Z)
• IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration [68.88711148515682]
  Deep neural networks are vulnerable to various 3D adversarial attacks. We propose an IF-Defense framework to directly optimize the coordinates of input points with geometry-aware and distribution-aware constraints. Our results show that IF-Defense achieves the state-of-the-art defense performance against existing 3D adversarial attacks on PointNet, PointNet++, DGCNN, PointConv and RS-CNN.
  arXiv  Detail & Related papers  (2020-10-11T15:36:40Z)
• ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds [78.25501874120489]
  We develop shape-aware adversarial 3D point cloud attacks by leveraging the learned latent space of a point cloud auto-encoder. Different from prior works, the resulting adversarial 3D point clouds reflect the shape variations in the 3D point cloud space while still being close to the original one.
  arXiv  Detail & Related papers  (2020-05-24T00:03:27Z)

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.