AIOZ AI Challenge One: Face Anti-Spoofing
Welcome to the Face Anti-Spoofing Challenge, where developers, researchers, and AI builders tackle a critical real-world problem in biometric security: spoof detection. Beat the Spoof!
1. Why This Challenge Matters
In today’s digital world, your face is more than an identity—it’s your password, boarding pass, and wallet. However, spoofing attacks from photos, screens, 3D masks, and deepfakes are undermining facial recognition systems.
Face spoofing threatens not just machines but trust in digital authentication itself.
2. Challenge Objective
Develop a model that can detect spoofing attempts without flagging real users.
The Face Anti-Spoofing Challenge invites the global AI community to take a stand by building models that can distinguish between real human faces and sophisticated forgeries across a variety of spoofing techniques and environments.
3. Challenge Description
Participants will receive different segments of the dataset in each phase, including a variety of spoofing formats such as:
- Printed photos
- Digital replays
- 3D masks
- Deepfake videos
- Other emerging spoofing techniques
The dataset also includes diverse samples of real faces with variations in:
- Lighting conditions
- Camera angles
- Backgrounds
- Demographics (age, gender, ethnicity)
Data split:
- Public dataset for development
- Private dataset for final evaluation
Download the dataset now:
https://aiozai.network/datasets/2c452335-148f-4591-9647-3b4ebe08301c
4. What Makes This Challenge Unique?
It’s not just about accuracy. Your model should balance the following to stand a chance of winning:
- Detection accuracy
- Lightweight model size
- Fast inference speed
These requirements are a test of performance, efficiency, and design—not just technical skill.
5. Evaluation Criteria
To reflect real-world usage, submissions will be judged based on:
- Accuracy: How well can the model distinguish spoofed faces from genuine ones?
- Model Size: Is the model lightweight enough to run on mobile/edge devices?
- Inference Time: Can it deliver real-time results in authentication workflows?
6. Submission Checklist
One account per participant. Each participant may submit one final entry for evaluation.
Your final submission must include:
- Trained model weights
- Inference script
- Full source code
- Clear documentation for setup & integration
7. Rewards
Total Challenge Rewards: 4,500 AIOZ tokens
- Special Reward: 2,000 AIOZ tokens
- Top 5 Participants: 500 AIOZ tokens each
Rewards will be distributed directly to winners’ wallets on the AIOZ Blockchain.
8. Timeline
Make sure to review the competition timeline carefully before jumping in:
| Challenge Phase | Duration | Start Date | End Date |
|---|---|---|---|
| Registration | 2 weeks | 25/06/2025 | 08/07/2025 |
| Training Phase | 1 week | 09/07/2025 | 15/07/2025 |
| Validation Phase | 2 weeks | 16/07/2025 | 29/07/2025 |
| Final Submission | 1 week | 30/07/2025 | 05/08/2025 |
| Judging Phase | 2 weeks | 06/08/2025 | 19/08/2025 |
| Result Announcement | 1 day | 20/08/2025 | 20/08/2025 |
Note:
- The Training Phase utilizes a public dataset accessible to all participants.
- The Validation Phase may involve partial or fully hidden datasets.
- The Final Submission Phase uses a completely hidden private dataset for evaluation.
Beat the spoof! This is your chance to showcase your skills in one of the best-emerging frontiers of computer vision.
Join the Face Anti-Spoofing Challenge today!
https://aiozai.network/challenges/face-anti-spoofing-challenge-3477
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