Physically Realizable Adversarial Examples for LiDAR Object Detection
arXiv:2004.00543
Abstract
Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial attacks with visually imperceptible perturbations. Despite the fact that this poses a security concern for the self-driving industry, there has been very little exploration in terms of 3D perception, as most adversarial attacks have only been applied to 2D flat images. In this paper, we address this issue and present a method to generate universal 3D adversarial objects to fool LiDAR detectors. In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%. We report attack results on a suite of detectors using various input representation of point clouds. We also conduct a pilot study on adversarial defense using data augmentation. This is one step closer towards safer self-driving under unseen conditions from limited training data.
Accepted to CVPR 2020
References in corpus (10)
- ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
- Provable defenses against adversarial examples via the convex outer adversarial polytope
- Synthesizing Robust Adversarial Examples
- Certified Defenses against Adversarial Examples
- Fast is better than free: Revisiting adversarial training
- Towards the first adversarially robust neural network model on MNIST
- Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction
- Adversarial Attack and Defense on Point Sets
- Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
- Adversarial shape perturbations on 3D point clouds