Given the threat of deepfakes, significant efforts have been made for proposing deepfake detection methods. Nevertheless, these methods remain not sufficiently mature for real-world deployment. as they usually specialize in detecting one type of deepfakes, which limits their generalization capability, and typically rely on very large models. Hence, UNFAKE aims to provide a more realistic deepfake detection framework that generalizes across different types of deepfakes by using an unsupervised explainable and low-weight learning framework to learn richer deep representations.
- Starting date: 01/09/2021
- Duration: 36 months + 12
- Funding source: FNR IF
- Researchers: Nesryne Mejri, Dr. Enjie Ghorbel, Prof. Djamila Aouada
- Partners: POST