{
  "id": "1807.10272",
  "version": "v1",
  "published": "2018-07-26T17:58:26.000Z",
  "updated": "2018-07-26T17:58:26.000Z",
  "title": "Evaluating and Understanding the Robustness of Adversarial Logit Pairing",
  "authors": [
    "Logan Engstrom",
    "Andrew Ilyas",
    "Anish Athalye"
  ],
  "comment": "Source code at https://github.com/labsix/adversarial-logit-pairing-analysis",
  "categories": [
    "stat.ML",
    "cs.CR",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "We evaluate the robustness of Adversarial Logit Pairing, a recently proposed defense against adversarial examples. We find that a network trained with Adversarial Logit Pairing achieves 0.6% accuracy in the threat model in which the defense is considered. We provide a brief overview of the defense and the threat models/claims considered, as well as a discussion of the methodology and results of our attack, which may offer insights into the reasons underlying the vulnerability of ALP to adversarial attack.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-07-26T17:58:26.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "robustness",
      "adversarial logit pairing achieves",
      "evaluating",
      "adversarial examples",
      "threat models/claims"
    ],
    "tags": [
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}