In this project, I focused on developing advanced methods to detect high-quality and unseen deepfakes, including those not encountered during training. Our approaches not only demonstrated superior performance on multiple datasets compared to state-of-the-art methods but also showed greater robustness to unseen common perturbations (e.g., Gaussian noise). Notably, our models require minimal computational overhead, underscoring their practical usefulness. This work has resulted in three publications, including two accepted at top-tier conferences in Computer Vision.
Please find more details of our algorithm with our 3 publications below:
@inproceedings{Nguyen_2025_ICCV,author={Nguyen, Dat and Astrid, Marcella and Kacem, Anis and Ghorbel, Enjie and Aouada, Djamila},title={Vulnerability-Aware Spatio-Temporal Learning for Generalizable Deepfake Video Detection},booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},month=oct,year={2025},pages={10786-10796},}
2024
Fakeformer: Efficient vulnerability-driven transformers for generalisable deepfake detection
Dat Nguyen, Marcella Astrid , Enjie Ghorbel , and 1 more author
arXiv preprint arXiv:2410.21964 - Under Review, Oct 2024
@inproceedings{Nguyen_2024_CVPR,author={Nguyen, Dat and Mejri, Nesryne and Singh, Inder Pal and Kuleshova, Polina and Astrid, Marcella and Kacem, Anis and Ghorbel, Enjie and Aouada, Djamila},title={LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},month=jun,year={2024},pages={17395-17405},}