Deepfake Detection
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.
