Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition

• URL: http://arxiv.org/abs/2412.13376v1
• Date: Tue, 17 Dec 2024 23:23:25 GMT
• Title: Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition
• Authors: Christian Green, Mehmet Ergezer, Abdurrahman Zeybey,
• Abstract summary: Adversarial attacks pose significant challenges in 3D object recognition. This paper introduces View-Invariant Adversarial Perturbations (VIAP), a novel method for crafting robust adversarial examples. We demonstrate the effectiveness of VIAP in both targeted and untargeted settings.
• Score: 1.7205106391379021
• License:
• Abstract: Adversarial attacks pose significant challenges in 3D object recognition, especially in scenarios involving multi-view analysis where objects can be observed from varying angles. This paper introduces View-Invariant Adversarial Perturbations (VIAP), a novel method for crafting robust adversarial examples that remain effective across multiple viewpoints. Unlike traditional methods, VIAP enables targeted attacks capable of manipulating recognition systems to classify objects as specific, pre-determined labels, all while using a single universal perturbation. Leveraging a dataset of 1,210 images across 121 diverse rendered 3D objects, we demonstrate the effectiveness of VIAP in both targeted and untargeted settings. Our untargeted perturbations successfully generate a singular adversarial noise robust to 3D transformations, while targeted attacks achieve exceptional results, with top-1 accuracies exceeding 95% across various epsilon values. These findings highlight VIAPs potential for real-world applications, such as testing the robustness of 3D recognition systems. The proposed method sets a new benchmark for view-invariant adversarial robustness, advancing the field of adversarial machine learning for 3D object recognition.

Related papers

• Open Vocabulary Monocular 3D Object Detection [10.424711580213616]
  We pioneer the study of open-vocabulary monocular 3D object detection, a novel task that aims to detect and localize objects in 3D space from a single RGB image. We introduce a class-agnostic approach that leverages open-vocabulary 2D detectors and lifts 2D bounding boxes into 3D space. Our approach decouples the recognition and localization of objects in 2D from the task of estimating 3D bounding boxes, enabling generalization across unseen categories.
  arXiv  Detail & Related papers  (2024-11-25T18:59:17Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds [16.604139389480615]
  Eidos is a framework providing Efficient Imperceptible aDversarial attacks on 3D pOint cloudS. This paper adds to the understanding of adversarial attacks by presenting Eidos, a framework providing Efficient Imperceptible aDversarial attacks on 3D pOint cloudS.
  arXiv  Detail & Related papers  (2024-05-23T06:09:08Z)
• One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation [1.4680035572775534]
  This paper presents a novel universal perturbation method for generating robust multi-view adversarial examples in 3D object recognition. Unlike conventional attacks limited to single views, our approach operates on multiple 2D images, offering a practical and scalable solution.
  arXiv  Detail & Related papers  (2024-04-02T20:29:59Z)
• Towards Unified 3D Object Detection via Algorithm and Data Unification [70.27631528933482]
  We build the first unified multi-modal 3D object detection benchmark MM- Omni3D and extend the aforementioned monocular detector to its multi-modal version. We name the designed monocular and multi-modal detectors as UniMODE and MM-UniMODE, respectively.
  arXiv  Detail & Related papers  (2024-02-28T18:59:31Z)
• Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing [28.874014617259935]
  Multi-Camera 3D Object Detection (MC3D-Det) has gained prominence with the advent of bird's-eye view (BEV) approaches. We propose a novel method that aligns 3D detection with 2D camera plane results, ensuring consistent and accurate detections.
  arXiv  Detail & Related papers  (2023-10-17T15:31:28Z)
• 3D Adversarial Augmentations for Robust Out-of-Domain Predictions [115.74319739738571]
  We focus on improving the generalization to out-of-domain data. We learn a set of vectors that deform the objects in an adversarial fashion. We perform adversarial augmentation by applying the learned sample-independent vectors to the available objects when training a model.
  arXiv  Detail & Related papers  (2023-08-29T17:58:55Z)
• Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training [57.25828870799331]
  We propose STMono3D, a new self-teaching framework for unsupervised domain adaptation on Mono3D. We develop a teacher-student paradigm to generate adaptive pseudo labels on the target domain. STMono3D achieves remarkable performance on all evaluated datasets and even surpasses fully supervised results on the KITTI 3D object detection dataset.
  arXiv  Detail & Related papers  (2022-04-25T12:23:07Z)
• Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving [87.3492357041748]
  In this paper, we showcase practical susceptibilities of multi-sensor detection by placing an adversarial object on top of a host vehicle. Our experiments demonstrate that successful attacks are primarily caused by easily corrupted image features. Towards more robust multi-modal perception systems, we show that adversarial training with feature denoising can boost robustness to such attacks significantly.
  arXiv  Detail & Related papers  (2021-01-17T21:15:34Z)
• SESS: Self-Ensembling Semi-Supervised 3D Object Detection [138.80825169240302]
  We propose SESS, a self-ensembling semi-supervised 3D object detection framework. Specifically, we design a thorough perturbation scheme to enhance generalization of the network on unlabeled and new unseen data. Our SESS achieves competitive performance compared to the state-of-the-art fully-supervised method by using only 50% labeled data.
  arXiv  Detail & Related papers  (2019-12-26T08:48:04Z)

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.