{
  "id": "2002.05388",
  "version": "v1",
  "published": "2020-02-13T08:40:48.000Z",
  "updated": "2020-02-13T08:40:48.000Z",
  "title": "Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks",
  "authors": [
    "Taro Kiritani",
    "Koji Ono"
  ],
  "comment": "11 pages, 4 figures",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Convolutional neural networks are vulnerable to small $\\ell^p$ adversarial attacks, while the human visual system is not. Inspired by neural networks in the eye and the brain, we developed a novel artificial neural network model that recurrently collects data with a log-polar field of view that is controlled by attention. We demonstrate the effectiveness of this design as a defense against SPSA and PGD adversarial attacks. It also has beneficial properties observed in the animal visual system, such as reflex-like pathways for low-latency inference, fixed amount of computation independent of image size, and rotation and scale invariance. The code for experiments is available at https://gitlab.com/exwzd-public/kiritani_ono_2020.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-02-13T08:40:48.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "adversarial attacks",
      "recurrent attention model",
      "log-polar mapping",
      "novel artificial neural network model",
      "human visual system"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 11,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}