{
  "id": "2202.07201",
  "version": "v1",
  "published": "2022-02-15T05:30:27.000Z",
  "updated": "2022-02-15T05:30:27.000Z",
  "title": "Holistic Adversarial Robustness of Deep Learning Models",
  "authors": [
    "Pin-Yu Chen",
    "Sijia Liu"
  ],
  "comment": "survey paper on holistic adversarial robustness for deep learning",
  "categories": [
    "cs.LG",
    "cs.AI",
    "cs.CR"
  ],
  "abstract": "Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. With the proliferation of deep-learning based technology, the potential risks associated with model development and deployment can be amplified and become dreadful vulnerabilities. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models, including attacks, defenses, verification, and novel applications.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-02-15T05:30:27.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep learning models",
      "holistic adversarial robustness",
      "adversarial robustness studies",
      "worst-case performance",
      "ensure safety"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}