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

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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

Silicone Mask Attack dataset

The dataset contains 6,500+ videos of attacks from 50 different people, filmed using 5 devices, providing a valuable resource for researching presentation attacks in facial recognition technologies. By focusing on this area, the dataset facilitates experiments designed to improve biometric security and anti-spoofing measures, ultimately aiding in the creation of more robust and reliable authentication systems. By utilizing this dataset, researchers can… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/silicone-mask-attack.

Textile 3D Face Mask Attack Dataset

This Dataset is specifically designed to enhance Face Anti-Spoofing and Liveness Detection models by simulating Nylon Mask Attacks — an accessible alternative to expensive silicone and latex mask datasets. These attacks utilize thin elastic fabric masks worn like a balaclava, featuring printed facial images that conform to the wearer's head shape through textile elasticity. The dataset is particularly valuable for PAD model training and iBeta… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/3d_cloth_face_mask_spoofing_dataset.

Latex Mask Dataset for Face Anti-Spoofing and Liveness Detection

Anti spoofing dataset with Latex 3D mask attacks (4 000 videos) for iBeta 2. The Biometric Attack Dataset offers a robust solution for enhancing security in liveness detection systems by simulating 3D latex mask attacks. This dataset is invaluable for assessing and fine-tuning Passive Liveness Detection models, an essential step toward achieving iBeta Level 2 certification. By integrating diverse realistic presentation… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/Latex_Mask_dataset.

Face mask dataset for facial recognition

This dataset contains over 11,100+ video recordings of people wearing latex masks, captured using 5 different devices.It is designed for liveness detection algorithms, specifically aimed at enhancing anti-spoofing capabilities in biometric security systems. By utilizing this dataset, researchers can develop more accurate facial recognition technologies, which is crucial for achieving the iBeta Level 2 certification, a benchmark for robust and… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/latex-mask-attack.

iBeta Level 2 PAD Anti-Spoofing (3D Masks) — Liveness Detection Training Dataset

Comprehensive biometric dataset for iBeta Level 2 liveness detection training and anti-spoofing research. Emphasis on 3D attacks (masks) and movements for Active liveness (zoom-in/zoom-out, micro-movements), high variability of devices and conditions, high diversity of subjects

  Spoofing Attack Types:

Silicone Mask Attacks Latex Mask Attacks Wrapped 3D Paper Mask Attacks Advanced Paper Mask… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/iBeta-Level-2-Certification-Dataset.