The dataset contains more than 363 original clips, with 28 actors performing in 16 different scenes. These high-quality videos provide a solid foundation for training models on real content.

In addition to the original data, the dataset also contains more than 3,000 processed videos generated using the DeepFakes method. These processed videos are crucial for training the model to accurately detect tampered content.

Dataset background

The Deepfake Detection dataset is designed for the deepfake detection task and provides a comprehensive collection of video sequences that can be used to train and evaluate deep learning models for identifying manipulated media. It is downloaded from the official FaceForensics server, which specializes in providing high-quality datasets for face manipulation detection.

The main inspiration of this dataset is to address the lack of availability of original video-based datasets required for deepfake detection. Most existing datasets focus on image-based manipulation, so there is a pressing need for accessible video datasets that can be used for model training and evaluation. This dataset fills this gap, providing a large amount of original and manipulated video content.

Use Cases

The Deepfake Detection dataset is suitable for:

• Training deep learning models to detect deepfake videos.
• Evaluating the performance of existing deepfake detection algorithms.
• Research and develop new techniques in the field of video-based manipulation detection.