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README.md

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 # Liveness Detection Dataset: 3D Paper Mask Attacks
 
-## Full version of the dataset is available for commercial usage. Leave a request on our website [Axonlabs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰
-## For feedback and additional sample requests, please contact us!
+Paper mask attack dataset for training PAD and liveness detection models against low-cost 3D spoofing — 2,000+ videos on iOS and Android, ISO 30107-3 Level 1/2 attack category
+
+## What This Dataset Covers
+
+2,000+ video recordings of 3D paper mask presentation attacks — masks with volumetric elements that simulate facial depth. Captured on iOS and Android devices, ~7 sec per video, with zoom-in/zoom-out phases for active liveness scenarios
 
+- 40+ participants
+- Balanced gender and ethnicity representation (Caucasian, Black, Asian)
+- Multiple device types: low-end to flagship
+
+## Attack Classification (ISO 30107-3)
+
+This attack type falls under **3D artifact attacks** in the ISO 30107-3 taxonomy — the same category tested in iBeta Level 1 and Level 2 certification. Paper masks with volume elements (protruding nose, forehead, shoulders) are one of the most accessible attack vectors, making them a baseline requirement for any PAD system
 ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2Fc1a66077f517a6165a102023b369e0c1%2FMerged.jpg?generation=1730402472776480&alt=media)
 
-## Dataset Description
-
-The **3D Paper Mask Attack Dataset** focuses on **3D volume-based paper attacks**, incorporating elements such as the nose, shoulders, and forehead. These attacks are designed to be advanced and are useful for both **PAD level 1** and **level 2** liveness tests. This dataset includes videos captured using various mobile devices and incorporates active liveness detection techniques.
-
-## Key Features
-- **40+ Participants**: Engaged in the dataset creation, with a balanced representation of Caucasian, Black, and Asian ethnicities.
-- **Video Capture**: Videos are captured on both **iOS and Android phones**, with **multiple frames** and **approximately 7 seconds** of video per attack.
-- **Active Liveness**: Includes a **zoom-in and zoom-out phase** to simulate active liveness detection.
-- **Diverse Scenarios**: 
-  - Options to add **volume-based elements** such as scarves, glasses, and hoodies.
-  - Captured using both **low-end and high-end devices**.
-  - Includes specific **attack scenarios** and **movements**, especially useful for **active liveness testing**.
-  - **Specific paper types** are used for attacks, contributing to the diversity of the dataset.
-
-## Ongoing Data Collection
-- This dataset is still in the data collection phase, and we welcome feedback and requests to incorporate additional features or specific requirements.
-
-## Potential Use Cases
-This dataset is ideal for training and evaluating models for:
-- **Liveness Detection**: Distinguishing between selfies and advanced spoofing attacks using 3D paper masks.
-- **iBeta Liveness Testing**: Preparing models for **iBeta** liveness testing, ensuring high accuracy in differentiating real faces from spoof attacks.
-- **Anti-Spoofing**: Enhancing security in biometric systems by identifying spoof attacks involving paper masks and other advanced methods.
-- **Biometric Authentication**: Improving facial recognition systems' resilience to sophisticated paper-based spoofing attacks.
-- **Machine Learning and Deep Learning**: Assisting researchers in developing robust liveness detection models for various testing scenarios.
+## How This Dataset Fits into Anti-Spoofing Pipelines
+
+- **Standalone:** Train a binary classifier (live vs. paper mask attack)
+- **Combined:** Merge with other attack types for a multi-class PAD model — pairs well with replay, print, and silicone mask data
+- **iBeta prep:** Use alongside live session data to simulate Level 1 certification test conditions
+
+
+## Related Datasets
+
+For a more advanced version of this attack type, see:  
+[**Wrapped 3D Paper Mask Spoofing Dataset**](https://huggingface.co/datasets/AxonData/Wrapped_3D_Attacks) — full-face masks 
+on mannequins with wigs and accessories
+
+For the complete anti-spoofing collection:  
+[**Face Anti-Spoofing Dataset**](https://huggingface.co/datasets/AxonData/face-anti-spoofing-dataset) — 100,000+ videos, 11 attack types.
+
+## Full version of the dataset is available for commercial usage. Leave a request on our website [Axonlabs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰
 
 ## Keywords
 - iBeta Certifications