{
  "id": "1909.08072",
  "version": "v1",
  "published": "2019-09-17T20:07:23.000Z",
  "updated": "2019-09-17T20:07:23.000Z",
  "title": "Adversarial Attacks and Defenses in Images, Graphs and Text: A Review",
  "authors": [
    "Han Xu",
    "Yao Ma",
    "Haochen Liu",
    "Debayan Deb",
    "Hui Liu",
    "Jiliang Tang",
    "Anil Jain"
  ],
  "comment": "25 pages, 10 more figures, survey paper",
  "categories": [
    "cs.LG",
    "cs.CR",
    "stat.ML"
  ],
  "abstract": "Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples raises our concerns in adopting deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for three most popular data types, including images, graphs and text.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-09-17T20:07:23.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "adversarial attacks",
      "deep neural networks",
      "adversarial examples raises",
      "popular data types",
      "dnn models"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 25,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}