Attending Generalizability in Course of Deep Fake Detection by Exploring Multi-task Learning


This work explores various ways of using 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.

In International Conference on Computer Vision Workshops 2023
Pranav Balaji
Pranav Balaji
Co Founder of Verbalia

My interests include deep learning, entrepreneurship and scuba diving.