{
  "id": "1708.03309",
  "version": "v1",
  "published": "2017-08-10T17:33:52.000Z",
  "updated": "2017-08-10T17:33:52.000Z",
  "title": "Systematic Testing of Convolutional Neural Networks for Autonomous Driving",
  "authors": [
    "Tommaso Dreossi",
    "Shromona Ghosh",
    "Alberto Sangiovanni-Vincentelli",
    "Sanjit A. Seshia"
  ],
  "categories": [
    "cs.CV",
    "cs.AI"
  ],
  "abstract": "We present a framework to systematically analyze convolutional neural networks (CNNs) used in classification of cars in autonomous vehicles. Our analysis procedure comprises an image generator that produces synthetic pictures by sampling in a lower dimension image modification subspace and a suite of visualization tools. The image generator produces images which can be used to test the CNN and hence expose its vulnerabilities. The presented framework can be used to extract insights of the CNN classifier, compare across classification models, or generate training and validation datasets.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-08-10T17:33:52.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "systematic testing",
      "autonomous driving",
      "lower dimension image modification subspace",
      "systematically analyze convolutional neural networks",
      "image generator"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}