{
  "id": "2109.05397",
  "version": "v1",
  "published": "2021-09-12T01:22:12.000Z",
  "updated": "2021-09-12T01:22:12.000Z",
  "title": "Challenges and Solutions in DeepFakes",
  "authors": [
    "Jatin Sharma",
    "Sahil Sharma"
  ],
  "comment": "6 Figures and 11 Tables",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Deep learning has been successfully appertained to solve various complex problems in the area of big data analytics to computer vision. A deep learning-powered application recently emerged is Deep Fake. It helps to create fake images and videos that human cannot distinguish them from the real ones and are recent off-shelf manipulation technique that allows swapping two identities in a single video. Technology is a controversial technology with many wide-reaching issues impacting society. So, to counter this emerging problem, we introduce a dataset of 140k real and fake faces which contain 70k real faces from the Flickr dataset collected by Nvidia, as well as 70k fake faces sampled from 1 million fake faces generated by style GAN. We will train our model in the dataset so that our model can identify real or fake faces.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-09-12T01:22:12.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "challenges",
      "contain 70k real faces",
      "70k fake faces",
      "big data analytics",
      "off-shelf manipulation technique"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}