Deepfake Datasets

Deepfake datasets are crucial for training and evaluating algorithms designed to detect manipulated media. Current research focuses on creating more diverse and realistic datasets that encompass a wider range of deepfake techniques, languages, and modalities (including audio and sign language), addressing limitations of existing datasets like FF++. This work aims to improve the generalizability and robustness of deepfake detection models, often employing convolutional neural networks and transformers, and mitigating biases related to demographic factors. The development of comprehensive benchmarks and standardized evaluation protocols is also a key area of focus, ultimately aiming to enhance the reliability and fairness of deepfake detection systems.