Face Anti-Spoofing Dataset — Your Essential Asset for AIOZ AI Challenge One
Overview
Achieving success in the AIOZ AI Challenge One: "Face Anti-Spoofing" starts with the right data.
This dataset captures a spectrum of presentation-attack techniques you are likely to encounter, including:
- Printed photos
- Digital replays
- 3D masks
- Deepfake videos
- Other emerging spoofing techniques
It also features real faces filmed under various lighting conditions, camera angles, backgrounds, and across diverse age, gender, and ethnic groups—providing a robust foundation for training, validation and evaluation.
What’s Inside
- Spoof Attacks: Includes common and advanced spoofing materials. This prepares your model to detect and defend against a wide range of real-world attack vectors.
- Genuine Faces: Captured in a variety of real-world settings using different devices, these samples help your model learn to recognize authentic inputs under diverse conditions.
Data split
- Public – Available for development and testing
- Private – Held back for final scoring to prevent overfitting
Why It Matters
- Comprehensive coverage – Combines classic spoofing methods with deepfake footage to enable models to detect attack vectors across wider timeframes.
- Real-world diversity – Various capture conditions ensure your model handles different devices and demographics.
- Challenge-ready – While participants will train and validate their models on the public split, what wins the challenge is how well the model generalizes beyond the visible data.
Get Started
- Download the provided dataset.
- Train, fine-tune, and validate your model.
- Submit your predictions on the AIOZ AI Challenge page.
- Test your solution against the private set and climb the leaderboard.
Build a model that spots the subtle cues between real and fake faces, and you’ll be one step closer to the podium in AIOZ AI Challenge One.
Join the Challenge Now:
https://aiozai.network/challenges/face-anti-spoofing-challenge-3477
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