{
  "id": "1907.05418",
  "version": "v1",
  "published": "2019-07-11T17:59:13.000Z",
  "updated": "2019-07-11T17:59:13.000Z",
  "title": "Adversarial Objects Against LiDAR-Based Autonomous Driving Systems",
  "authors": [
    "Yulong Cao",
    "Chaowei Xiao",
    "Dawei Yang",
    "Jing Fang",
    "Ruigang Yang",
    "Mingyan Liu",
    "Bo Li"
  ],
  "categories": [
    "cs.CR",
    "cs.CV",
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions. Recent studies show that adversarial examples can pose a threat to real-world security-critical applications: a \"physical adversarial Stop Sign\" can be synthesized such that the autonomous driving cars will misrecognize it as others (e.g., a speed limit sign). However, these image-space adversarial examples cannot easily alter 3D scans of widely equipped LiDAR or radar on autonomous vehicles. In this paper, we reveal the potential vulnerabilities of LiDAR-based autonomous driving detection systems, by proposing an optimization based approach LiDAR-Adv to generate adversarial objects that can evade the LiDAR-based detection system under various conditions. We first show the vulnerabilities using a blackbox evolution-based algorithm, and then explore how much a strong adversary can do, using our gradient-based approach LiDAR-Adv. We test the generated adversarial objects on the Baidu Apollo autonomous driving platform and show that such physical systems are indeed vulnerable to the proposed attacks. We also 3D-print our adversarial objects and perform physical experiments to illustrate that such vulnerability exists in the real world. Please find more visualizations and results on the anonymous website: https://sites.google.com/view/lidar-adv.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-07-11T17:59:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "adversarial objects",
      "lidar-based autonomous driving systems",
      "adversarial examples",
      "autonomous driving detection systems",
      "apollo autonomous driving platform"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}