---
license: cc-by-4.0
task_categories:
- image-feature-extraction
- image-classification
- video-classification
language:
- en
tags:
- silicone mask
- silicone mask attack
- 3d mask
- biometric security
- attack detection
- liveness detection
- anti-spoofing
- biometrics
- facial recognition
- iBeta certification
- PAD attack
- security
- ibeta
- face recognition
size_categories:
- 10K<n<100K
---
# 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

## 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 💰

![](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.

## 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.

- Additional Flexibility: We can recreate this dataset using both RGB and USB camera inputs to accommodate various research needs.


## 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.

## 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.

## 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.