A Unified Framework for Adversarial Patch Attacks Against Visual 3D Object Detection in Autonomous Driving

The rapid development of vision-based 3D perceptions, in conjunction with the inherent vulnerability of deep neural networks to adversarial examples, motivates us to investigate realistic adversarial attacks for the 3D detection models in autonomous driving scenarios. Due to the perspective transformation from 3D space to the image and object occlusion, current 2D image attacks are difficult to generalize to 3D detectors and are limited by physical feasibility. In this work, we propose a unified framework to generate physically printable adversarial patches with different attack goals: 1) instance-level hiding—pasting the learned patches to any target vehicle allows it to evade the detection process; 2)…

• FR
  Fundamental Research Funds for the Central Universities

  Award: xzy022023053
• MS
  Major Science and Technology Projects in Yunnan Province

  Award: 2022A02012-2