A forensics dataset for detecting manipulated facial images and videos.

FaceForensics++ is a comprehensive dataset designed for training and evaluating algorithms for detecting manipulated facial images and videos. It includes 1000 original video sequences sourced from YouTube, manipulated using four different automated face manipulation methods: Deepfakes, Face2Face, FaceSwap, and NeuralTextures. The dataset provides binary masks for segmentation tasks, making it suitable for both image and video classification. It also offers 1000 Deepfakes models for generating and augmenting new data. The Deep Fake Detection Dataset, provided by Google & JigSaw, is also included. The dataset is valuable for research in media forensics, security, and combating the spread of misinformation through manipulated media.
FaceForensics++ is a comprehensive dataset designed for training and evaluating algorithms for detecting manipulated facial images and videos.
Explore all tools that specialize in identify deepfakes. This domain focus ensures FaceForensics++ delivers optimized results for this specific requirement.
Explore all tools that specialize in identify face swaps. This domain focus ensures FaceForensics++ delivers optimized results for this specific requirement.
Explore all tools that specialize in generate deepfake models. This domain focus ensures FaceForensics++ delivers optimized results for this specific requirement.
Includes Deepfakes, Face2Face, FaceSwap, and NeuralTextures manipulation methods, offering a diverse range of forgery types.
Provides binary masks for each manipulated face, facilitating pixel-level segmentation and analysis.
Offers 1000 Deepfakes models for generating new manipulated data, enhancing dataset augmentation capabilities.
Includes the Deep Fake Detection Dataset by Google & JigSaw, expanding the dataset's scale and diversity.
Offers an automated benchmark for evaluating facial manipulation detection algorithms on unseen data.
1. Fill out the Google Form to request access to the dataset.
2. Await approval and receive the download link.
3. Download the provided download script.
4. Execute the download script to obtain the dataset.
5. Follow the dataset documentation for file structure and usage.
6. Preprocess the data if required (e.g., resizing, normalization).
7. Implement your chosen deepfake detection algorithm.
8. Train your model on the FaceForensics++ dataset.
9. Evaluate your model using the provided benchmark.
All Set
Ready to go
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