On the Adversarial Robustness of Camera-Based 3D Object Detection

Details

• Creators:
  Xie, Shaoyuan ; Li, Zichao ; Wang, Zeyu ; Xie, Cihang
• Corporate Creators:
  University of California
• Corporate Contributors:
  Open Philanthropy ; National Center for Transportation Cybersecurity and Resiliency (TraCR) National University Transportation Center (UTC) ; Clemson University
• Subject/TRT Terms:
  Autonomous Vehicles Cameras Detectors By Object Of Detection Safety Visualization
• Resource Type:
  Tech Report
• Right Statement:
  Open access
• Geographical Coverage:
  United States
• Corporate Publisher:
  University of California, Irvine.
• Abstract:
  In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost. However, the robustness of these methods to adversarial attacks has not been thoroughly examined, especially when considering their deployment in safety-critical domains like autonomous driving. In this study, we conduct the first comprehensive investigation of the robustness of leading camera-based 3D object detection approaches under various adversarial conditions. We systematically analyze the resilience of these models under two attack settings: white-box and black-box; focusing on two primary objectives: classification and localization. Additionally, we delve into two types of adversarial attack techniques: pixel-based and patch-based. Our experiments yield four interesting findings: (a) bird’s-eye-view-based representations exhibit stronger robustness against localization attacks; (b) depth-estimation-free approaches have the potential to show stronger robustness; (c) accurate depth estimation effectively improves robustness for depth-estimation-based methods; (d) incorporating multi-frame benign inputs can effectively mitigate adversarial attacks. We hope our findings can steer the development of future camera-based object detection models with enhanced adversarial robustness. The code is available at: https://github.com/Daniel-xsy/BEV-Attack.
• Content Notes:
  Published in Transactions on Machine Learning Research (01/2024); Reviewed on OpenReview: https: // openreview. net/ forum? id= 6SofFlwhEv
• Format:
  PDF
• Collection(s):
  Autonomous Vehicles US Transportation Collection
• Main Document Checksum:
  urn:sha-512:f15ad8b030a3865b1191ad27adf1b1b6a3167469fe6259ff81c7c5eb22c787296765959067fc859c708245b75ad6a239307ae9cedcc2d57cec4d5d407c4d0ed0
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73429/dot_73429_DS1.pdf
• File Type:
  [PDF - 2.65 MB ]