Face Antispoofing dataset for liveness detection

Anti-Spoofing dataset: live, replay, cut, print, 3D masks - large-scale face anti spoofing This dataset delivers a single, end-to-end resource for training and benchmarking facial liveness-detection systems. By aggregating live sessions and eleven realistic presentation-attack classes into one collection, it accelerates development toward iBeta Level 1/2 compliance and strengthens model robustness against the full spectrum of spoofing tactics

##Why Comprehensive Anti-Spoofing Data? Modern certification pipelines demand proof that a system resists all common attack vectors—not just prints or replays. This dataset delivers those vectors in one place, allowing you to:

Benchmark a model’s true generalisation
Fine-tune against rare but high-impact threats (e.g., silicone or textile masks)
Streamline audits by demonstrating coverage of every ISO 30107-3 attack category

##Dataset Features

Dataset Size: ≈ 95 000 videos / image sequences spanning live captures and eleven spoof classes
Attack Diversity: 3D paper mask, wrapped 3D mask, photo print, mobile replay, display replay, cut-out 2D mask, silicone mask, latex mask, textile mask
Active Liveness Cues: Natural blinks, and head rotations included across live and mask sessions
Attribute Range: different combinations of hairstyles, eyewear, facial hair, and accessories.
Environmental Variability: Indoor/outdoor scenes under various lighting conditions
Multi-angle Capture: Mainly used selfie camera, also back
Capture Devices: Footage from flagship and mid-range phones (iPhone 14 / 13 Pro, Galaxy S23, Pixel 7, Redmi Note 12 Pro+, Galaxy A54, Honor 70)
Additional Flexibility: Custom re-captures available on request

Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset 💰

Technical Specifications

File Format: MP4 for video, JPEG/PNG for still sequences; all compatible with mainstream ML frameworks
Resolution & FPS: Up to 4K @ 60 fps; balanced presets included for rapid training

Best Uses

Ideal for companies pursuing or maintaining iBeta Level 1/2 certification, research groups exploring new PAD architectures, and vendors stress-testing production face-verification pipelines

Attack Classes

Live / Genuine Natural faces with spontaneous movements across varied devices and lighting
3D Paper Mask Folded paper masks with protruding nose/forehead
Wrapped 3D Print Rigid paper moulds reproducing head geometry
Photo Print Glossy still photos at multiple angles—the classic 2D spoof
Cylinder 3D Paper Mask A folded or cylindrical sheet of paper that simulates volume
Mobile Replay Face videos played on phone screens; includes glare and auto-brightness shifts
Display Replay Attacks via monitors, and laptops
Cut-out 2D Mask Flat printed masks with eye/mouth holes plus active head motion
On-actor Print / Cuts Paper elements (photos, cutouts) are glued directly onto the actor's face
Silicone and Latex Masks High-detail silicone/latex overlays with blinking and subtle mimicry
Cloth 3D Mask Elastic fabric masks hugging facial contours during movement
High-Fidelity Resin Mask Hyperrealistic masks with detailed skin texture

Conclusion

This dataset’s scale, breadth of attack types, and real-world capture conditions make it indispensable for anyone building or evaluating biometric anti-spoofing solutions. Deploy it to harden your systems against today’s—and tomorrow’s—most sophisticated presentation attacks

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