mlsanityData to 14 Oct 2026

Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models

arXiv 2109.06363 ↗First posted 13 Sep 2021cs.CV cs.LGcited by 5

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Abstract (arXiv)

A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs. In this work, we perform the first study to analyze the robustness of a high-performance, open source sensor fusion model architecture towards adversarial attacks and challenge the popular belief that the use of additional sensors automatically mitigate the risk of adversarial attacks. We find that despite the use of a LIDAR sensor, the model is vulnerable to our purposefully crafted image-based adversarial attacks including disappearance, universal patch, and spoofing. After identifying the underlying reason, we explore some potential defenses and provide some recommendations for improved sensor fusion models.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 22 references that are in the map. First posted on arXiv 13 Sep 2021.

Builds on

16 of 22 references.

References · 16

  1. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  2. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  3. 2016Universal adversarial perturbationscited by 784
  4. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 724
  5. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  6. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 326
  7. 2017Adversarial Examples for Semantic Segmentation and Object Detectioncited by 275
  8. 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
  9. 2018Physical Adversarial Examples for Object Detectorscited by 163
  10. 2019Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Drivingcited by 152
  11. 2020Physically Realizable Adversarial Examples for LiDAR Object Detectioncited by 137
  12. 2020Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasurescited by 95
4 more
  1. 2018Generalizable Data-free Objective for Crafting Universal Adversarial Perturbationscited by 88
  2. 2019Towards Adversarially Robust Object Detectioncited by 55
  3. 2019On Single Source Robustness in Deep Fusion Modelscited by 12
  4. 2019Geometry-Aware Generation of Adversarial Point Cloudscited by 6

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Cited by year

Citing papers per year: 2023: 4; 2024: 1.

5 papers.

  1. 2023Robo3D: Towards Robust and Reliable 3D Perception against Corruptionscited by 129
  2. 2023Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Drivingcited by 37
  3. 2023Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detectioncited by 11
  4. 2024A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detectioncited by 3
  5. 2023Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Drivingcited by 1