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README.md

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-# Silicone Mask Attack Dataset - 10,000+ Videos
-10,000+ videos of attacks with Silicone 3D Masks for iBeta 2. The Dataset is designed to address security challenges in liveness detection systems through 3D silicone mask attacks. These presentation attacks are key for testing Passive Liveness Detection systems crucial for iBeta Level 2 certification. This dataset significantly enhances the capabilities of liveness detection models
+# Silicone Mask Attack Dataset — 10,000+ Videos for Anti-Spoofing
 
-## Full version of dataset is available for commercial usage - leave a request on our website [Axon Labs](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰
+Anti-spoofing dataset with 10,000+ attack videos featuring 18 hyper-realistic silicone masks. Designed for training liveness detection and presentation attack detection (PAD) models targeting **iBeta Level 2** certification.
+
+Covers 8 devices, 5 shooting angles, ~40 attribute combinations (wigs, glasses, beards), and diverse real-world environments — offices, apartments, and outdoor locations.
+
+## Key Features
+
+- **3D mask attacks only** — purely high-fidelity silicone mask presentations, not photos or screen replays
+- **Scale** — 10,000+ videos provide sufficient data for deep learning approaches without heavy augmentation
+- **Demographic diversity** — 18 masks spanning male/female, Caucasian/Asian appearances
+- **Real-world variability** — recorded in offices, apartments, and outdoor scenes, not just lab conditions
+
+Full dataset is available for commercial licensing — [request access on Axon Labs website](https://axonlab.ai/). This repository contains a preview sample.
 
 ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2Fca8aefa134ecb71d5d5fb75ea7a34f30%2FFrame%2074.png?generation=1743576725932098&alt=media)
 
 Successfull Spoofing attack on a Liveness test by [Duobango ](https://www.doubango.org/webapps/face-liveness/)
 
-## Why Silicone Mask Data?
-This dataset is crucial for companies preparing to comply with iBeta Level 2 certification which requires anti-spoofing technologies. In today's digital security landscape, the Silicone Mask Dataset serves as a critical resource for training Machine Learning (ML) models and advanced biometric techniques to detect spoofing attempts.
+## Recording Conditions
+
+### Capture Devices (8 models)
+iPhone 14, iPhone 14 Pro, iPhone 13 Pro, Samsung Galaxy S23, Samsung Galaxy A54, Google Pixel 7, Xiaomi Redmi Note 12 Pro+, Honor 70
+
+### Shooting Angles (5 views)
+Front selfie, back camera close-up, back camera far, left side, right side
+
+### Attribute Variations (~40 combinations)
+Each mask is recorded with combinations of wigs, glasses, beards, and different hairstyles — simulating how real attackers modify mask appearance to bypass detection.
+
+### Active Liveness Challenges
+Videos include natural head movements and blinking to specifically test active liveness detection pipelines that rely on motion-based cues.
+
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F68efd6a980a88f0bc9f8852d0fb8cede%2F2025-03-31%20%2012.04.55.png?generation=1743411918818968&alt=media)
+
+## Intended Use Cases
 
-## Dataset Features
-- Variety of Masks: Encompasses 18 unique silicone masks (male and female, Caucasian ans Asian ethnicity)
-- Video Collection: There are roughly 10,000 videos that showcase detailed spoofing detection scenarios.
-- Capture Devices: Two different recording devices in selfie mode to mirror real-life conditions.
-- Environmental Conditions: Captures videos across diverse lighting and background settings to ensure robustness.
+**Training PAD classifiers** — Use as attack samples paired with your genuine (bona fide) data to train binary or multi-class anti-spoofing models.
 
-- Additional Flexibility: We can recreate this dataset using both RGB and USB camera inputs to accommodate various research needs.
+**Benchmarking liveness detection** — Evaluate existing models against high-quality 3D mask attacks to identify failure modes before iBeta testing.
 
+**Multi-modal fusion research** — Combine with depth, IR, or thermal data to study cross-modal attack detection strategies.
 
-## 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.
+**Adversarial robustness testing** — The ~40 attribute combinations (glasses, wigs, beards) let you test model robustness against disguise variations.
 
-## Best Uses
-This dataset is ideal for entities striving to meet or exceed iBeta Level 2 certification. By integrating this dataset, organizations can greatly enhance the training effectiveness of anti-spoofing algorithms, ensuring a robust and accurate performance in practical scenarios.
+## Related Datasets by Axon Labs
 
-## Conclusion
-With its comprehensive features and simulation of real attack scenarios, the Silicone Mask Biometric Attack Dataset is indispensable for anyone involved in developing and certifying facial recognition and liveness detection technologies. Utilize this dataset to strengthen your systems against the most deceptive digital security threats.
+- [Latex Mask Attack Dataset](https://huggingface.co/datasets/AxonData/Latex-Mask-Attack-Dataset) — 4,000 videos with latex masks
+- [Paper Mask Attack Dataset](https://huggingface.co/datasets/AxonData/Paper-Mask-Attack-Dataset) — 2,500 videos with wrapped 3D paper masks
+- [iBeta Level 2 Full Dataset](https://huggingface.co/datasets/AxonData/iBeta-Level-2-Dataset) — 25,000+ combined attack videos