Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples

• URL: http://arxiv.org/abs/2307.16361v2
• Date: Thu, 10 Aug 2023 02:45:55 GMT
• Title: Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples
• Authors: Qiufan Ji, Lin Wang, Cong Shi, Shengshan Hu, Yingying Chen, Lichao Sun
• Abstract summary: adversarial examples on 3D point clouds make them more challenging to defend against than those on 2D images. In this paper, we first establish a comprehensive, and rigorous point cloud adversarial robustness benchmark. We then perform extensive and systematic experiments to identify an effective combination of these tricks. We construct a more robust defense framework achieving an average accuracy of 83.45% against various attacks.
• Score: 25.029854308139853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Neural Networks (DNNs) for 3D point cloud recognition are vulnerable to adversarial examples, threatening their practical deployment. Despite the many research endeavors have been made to tackle this issue in recent years, the diversity of adversarial examples on 3D point clouds makes them more challenging to defend against than those on 2D images. For examples, attackers can generate adversarial examples by adding, shifting, or removing points. Consequently, existing defense strategies are hard to counter unseen point cloud adversarial examples. In this paper, we first establish a comprehensive, and rigorous point cloud adversarial robustness benchmark to evaluate adversarial robustness, which can provide a detailed understanding of the effects of the defense and attack methods. We then collect existing defense tricks in point cloud adversarial defenses and then perform extensive and systematic experiments to identify an effective combination of these tricks. Furthermore, we propose a hybrid training augmentation methods that consider various types of point cloud adversarial examples to adversarial training, significantly improving the adversarial robustness. By combining these tricks, we construct a more robust defense framework achieving an average accuracy of 83.45\% against various attacks, demonstrating its capability to enabling robust learners. Our codebase are open-sourced on: \url{https://github.com/qiufan319/benchmark_pc_attack.git}.

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)
• Improving Adversarial Robustness via Decoupled Visual Representation Masking [65.73203518658224]
  In this paper, we highlight two novel properties of robust features from the feature distribution perspective. We find that state-of-the-art defense methods aim to address both of these mentioned issues well. Specifically, we propose a simple but effective defense based on decoupled visual representation masking.
  arXiv  Detail & Related papers  (2024-06-16T13:29:41Z)
• Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks [62.036798488144306]
  Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. We propose an attack-agnostic defense method named Meta Invariance Defense (MID) We show that MID simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration.
  arXiv  Detail & Related papers  (2024-04-04T10:10:38Z)
• Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds [62.94859179323329]
  Adrial attack methods based on point manipulation for 3D point cloud classification have revealed the fragility of 3D models. We propose a novel shape-based adversarial attack method, HiT-ADV, which conducts a two-stage search for attack regions based on saliency and imperceptibility perturbation scores. We propose that by employing benign resampling and benign rigid transformations, we can further enhance physical adversarial strength with little sacrifice to imperceptibility.
  arXiv  Detail & Related papers  (2024-03-08T12:08:06Z)
• Passive Defense Against 3D Adversarial Point Clouds Through the Lens of 3D Steganalysis [1.14219428942199]
  A 3D adversarial point cloud detector is designed through the lens of 3D steganalysis. To our knowledge, this work is the first to apply 3D steganalysis to 3D adversarial example defense.
  arXiv  Detail & Related papers  (2022-05-18T06:19:15Z)
• 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)
• Generating Unrestricted 3D Adversarial Point Clouds [9.685291478330054]
  deep learning for 3D point clouds is still vulnerable to adversarial attacks. We propose an Adversarial Graph-Convolutional Generative Adversarial Network (AdvGCGAN) to generate realistic adversarial 3D point clouds.
  arXiv  Detail & Related papers  (2021-11-17T08:30:18Z)
• Minimal Adversarial Examples for Deep Learning on 3D Point Clouds [25.569519066857705]
  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 achieves the state-of-the-art performance with higher than 89% and 90% of attack success rate on synthetic and real-world data respectively.
  arXiv  Detail & Related papers  (2020-08-27T11:50:45Z)
• Robust Tracking against Adversarial Attacks [69.59717023941126]
  We first attempt to generate adversarial examples on top of video sequences to improve the tracking robustness against adversarial attacks. We apply the proposed adversarial attack and defense approaches to state-of-the-art deep tracking algorithms.
  arXiv  Detail & Related papers  (2020-07-20T08:05:55Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)
• Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks [65.20660287833537]
  In this paper we propose two extensions of the PGD-attack overcoming failures due to suboptimal step size and problems of the objective function. We then combine our novel attacks with two complementary existing ones to form a parameter-free, computationally affordable and user-independent ensemble of attacks to test adversarial robustness.
  arXiv  Detail & Related papers  (2020-03-03T18:15:55Z)

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.