Attending Generalizability in Course of Deep Fake Detection by Exploring
  Multi-task Learning
Pranav Balaji Abhijit Das Srijan Das Antitza Dantcheva
This work explores various ways of exploring multi-task learning (MTL)
techniques aimed at classifying videos as original or manipulated in
cross-manipulation scenario to attend generalizability in deep fake scenario.
The dataset used in our evaluation is FaceForensics++, which features 1000
original videos manipulated by four different techniques, with a total of 5000
videos. We conduct extensive experiments on multi-task learning and contrastive
techniques, which are well studied in literature for their generalization
benefits. It can be concluded that the proposed detection model is quite
generalized, i.e., accurately detects manipulation methods not encountered
during training as compared to the state-of-the-art.