Introduction
This dataset is designed to enhance Liveness Detection models by simulating Wrapped 3D Attacks — a more advanced version of 3D Print Attacks, where facial prints include 3D elements and additional attributes. It is particularly useful for iBeta Level 2 certification and anti-spoofing model training
Dataset summary
- Dataset Size: ~2k videos shoot on 20 IDs demonstrating various spoofing attacks
- Active Liveness Features:Â Includes zoom-in and zoom-out to enhance training scenarios
- Attributes: Different hairstyles, glasses, wigs and beards to enhance diversity
- Variability: 3 indoor locations with different types of lighting: low, medium and bright
- Main Applications: Preparation for iBeta Level 2 certification, Active and passive liveness for anti spoofing systemsÂ
Source and collection methodology
The videos capture realistic spoofing conditions using different recording devices and variety of environments. Additionally, each attack video employs a zoom-in effect, adding to its effectiveness in active liveness detection. The videos were shot using a back-facing camera
Â
To create wrapped 3D attacks, we:
Constructed 3D facial structures by cutting out A4-sized face prints, shaping volume for the nose, forehead, and chin, and mounting them on mannequin heads or cylindrical objects
Added attributes, including wigs, beards, mustaches, glasses, hats, and hoods, to increase spoofing complexity
Simulated a human torso using clothing on mannequins, chairs, or surfaces
Use cases and applications
iBeta Level 2 Certification Compliance:Â
- Helps to train the models for iBeta level 2 certification tests
- Allows pre-certification testing to assess system performance before submission
Inhouse Liveness Detection Models:Â
- Used for training and validation of anti-spoofing models
- Enables testing of existing algorithms and identification of their vulnerabilities against spoofing attacks
Who is this for?
- AI/ML teams – Train custom anti-spoofing models for security applications
- Identity verification providers – Ensure fraud prevention in KYC & financial services
- Financial institutions – Implement internal e-KYC solutionsÂ
File format and accessibility
- Format: Videos are optimized for compatibility with mainstream ML frameworks
- Resolution and frame rate: Videos are high-resolution with frame rates calibrated for capturing quick and realistic mask placements, ensuring precise data for model training
Potential customisation options:
- Filming videos attacks with targeted movements (E.g. – Zoom In / Zoom Out)
- Filming videos attacks for you on target devices (for example, webcams)
- Using your SDK for custom attack scenarios spoofing your ML model
- Use RGB and USB cameras to support diverse research and testing needs
Legal & Compliance
We prioritize data privacy, ethical AI development, and regulatory compliance. Our Silicone Mask Attack Dataset is collected and processed in full accordance with global data protection standards including GDPR, ensuring legality, security, and responsible AI practices
Sample dataset
A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below
Contact us
Tell us about yourself, and get access to free samples of the datasetÂ
