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README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+tags:
+  - liveness detection
+  - anti-spoofing
+  - biometrics
+  - facial recognition
+  - machine learning
+  - deep learning
+  - AI
+  - paper mask attack
+  - iBeta certification
+  - PAD attack
+  - security
+  - ibeta
+  - face recognition
+  - pad
+  - authentication
+  - fraud
+task_categories:
+  - video-classification
+---
+## 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](https://huggingface.co/datasets/AxonData/iBeta_level_2_Silicone_masks)
+- [Latex Mask Attacks](https://huggingface.co/datasets/AxonData/Latex_Mask_dataset)
+- [Wrapped 3D Paper Mask Attacks](https://huggingface.co/datasets/AxonData/Wrapped_3D_Attacks)
+- [Advanced Paper Mask Attacks](https://huggingface.co/datasets/AxonData/face-anti-spoofing-advanced-paper-attacks)
+- [Cloth 3D Face Mask Attacks](https://huggingface.co/datasets/AxonData/3d_cloth_face_mask_spoofing_dataset)
+
+These reflect spoofing attack types commonly explored in iBeta PAD Level 2 test plans
+
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F23520c4cb0c7175fb3a135c8cbd59072%2F2025-08-06%2017.51.14.jpg?generation=1754484802643096&alt=media)
+
+# Dataset Description: 
+- 25,000+ videos (~10 sec), multiframe
+- Each attack is captured on iOS and Android phone
+- Zoom in and zoom out phase for Active Liveness
+
+## Full version of dataset is availible for commercial usage - leave a request on our website [Axonlabs ](https://axonlabs.pro/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link)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
+
+##Technical Specifications
+•	**File Format:** Videos are formatted to be compatible with mainstream ML frameworks
+
+•	**Resolution and Frame Rate:** Tailored for high-resolution and optimal frame rates to capture quick mask placements
+
+keywords: ibeta level 1, ibeta level 2, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, face detetction, face identificarion, face recognition, face id, attacks detection dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, presentation attack detection, presentation attack dataset, 2D print attacks, 3D print attacks, silicone masks attacks, video dataset, video classification, computer vision, deep learning, machine learning, phone attack dataset, face anti spoofing, replay spoof attack, cut prints spoof attack, large-scale face anti spoofing, rich annotations anti spoofing dataset, display spoof attack