{
  "id": "2008.09194",
  "version": "v1",
  "published": "2020-08-20T20:25:18.000Z",
  "updated": "2020-08-20T20:25:18.000Z",
  "title": "Not My Deepfake: Towards Plausible Deniability for Machine-Generated Media",
  "authors": [
    "Baiwu Zhang",
    "Jin Peng Zhou",
    "Ilia Shumailov",
    "Nicolas Papernot"
  ],
  "categories": [
    "cs.LG",
    "cs.CR",
    "cs.CV",
    "cs.CY"
  ],
  "abstract": "Progress in generative modelling, especially generative adversarial networks, have made it possible to efficiently synthesize and alter media at scale. Malicious individuals now rely on these machine-generated media, or deepfakes, to manipulate social discourse. In order to ensure media authenticity, existing research is focused on deepfake detection. Yet, the very nature of frameworks used for generative modeling suggests that progress towards detecting deepfakes will enable more realistic deepfake generation. Therefore, it comes at no surprise that developers of generative models are under the scrutiny of stakeholders dealing with misinformation campaigns. As such, there is a clear need to develop tools that ensure the transparent use of generative modeling, while minimizing the harm caused by malicious applications. We propose a framework to provide developers of generative models with plausible deniability. We introduce two techniques to provide evidence that a model developer did not produce media that they are being accused of. The first optimizes over the source of entropy of each generative model to probabilistically attribute a deepfake to one of the models. The second involves cryptography to maintain a tamper-proof and publicly-broadcasted record of all legitimate uses of the model. We evaluate our approaches on the seminal example of face synthesis, demonstrating that our first approach achieves 97.62% attribution accuracy, and is less sensitive to perturbations and adversarial examples. In cases where a machine learning approach is unable to provide plausible deniability, we find that involving cryptography as done in our second approach is required. We also discuss the ethical implications of our work, and highlight that a more meaningful legislative framework is required for a more transparent and ethical use of generative modeling.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-08-20T20:25:18.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "generative model",
      "plausible deniability",
      "machine-generated media",
      "ensure media authenticity",
      "realistic deepfake generation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}