video
video | label
class label 2
classes |
|---|---|
0Galaxy_a54
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0Galaxy_a54
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0Galaxy_a54
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1iPhone_12
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0Galaxy_a54
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0Galaxy_a54
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1iPhone_12
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1iPhone_12
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Liveness Detection Dataset — iBeta Level 3 Preparation: High-Fidelity Mask Attacks (1000+)
Contact us with your feedback - recieve additional samples for free!😊
Train for the newest iBeta Level 3 today
This is our first iBeta-3 dataset, and we’re actively expanding it so you can start preparing right now and stay ahead as the standard evolves
Why this dataset?
- Built for iBeta-3 difficulty. Active zoom-in/zoom-out prompts and high-fidelity PAD scenarios aligned to the newest certification level
- First to market & growing. Our first iBeta-3 dataset, actively expanding—train today and stay ahead as standards evolve
- State-of-the-art premium mask. An advanced rubber mask; we’re the first to release datasets of this kind and provide free samples
Dataset Description:
- 5+ unique masks in the project
- Each attack is captured on iOS and Android phone, multiple frames, ~10 sec videos
- Diverse Representation: Balanced mix of genders
- Zoom in and zoom out phase for Active Liveness
See the difference
We also have latex/silicone mask sets, but this one is far more detailed and realistic - built specifically to stress-test PAD at iBeta-3 difficulty
Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset 💰
Potential Use Cases:
Liveness detection: This dataset is ideal for training and evaluating liveness detection models, enabling researchers to distinguish between selfies and spoof attacks with high accuracy
iBeta liveness testing: This dataset is valuable for training and evaluating liveness detection models before applying to iBeta certifications, enabling researchers to distinguish between selfies and spoof attacks with high accuracy
Learn the iBeta-3 context
We published one of the few structured overviews of iBeta Level 3 requirements and test design: https://axonlabs.pro/ibeta3-liveness-detection-face-anti-spoofing/
Keywords: iBeta certifications, PAD attacks, Presentation Attack Detection, Antispoofing, Liveness Detection, Spoof Detection, Facial Recognition, Biometric Authentication, Security Systems, AI Dataset, Replay Attack Dataset, Anti-Spoofing Technology, Facial Biometrics, Machine Learning Dataset, Deep Learning
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