Introduction
Silicone Mask Biometric Attack Dataset offers a robust solution for enhancing security in liveness detection systems by simulating 3D silicone mask attacks. This dataset is invaluable for assessing and fine-tuning Passive Liveness Detection models, an essential step toward achieving iBeta Level 2 certification. By integrating diverse realistic presentation attacks (PAD), this dataset significantly supports advancements in detecting biometric anti spoofing
Dataset summary
- Dataset Size: ~10,000 videos demonstrating various spoofing attacks; 18 high-detail silicone masks
- Active Liveness Features: Includes natural head movements and blinking to enhance training scenarios
- Attributes: Different hairstyles, glasses, wigs and beards to enhance diversity, ~40 combinations of attributes
- Variability: 4 office spaces, 4 home apartments and 2 outdoor locations. 3 Types of Lighting: Low, Medium, Bright
- Main Applications: Preparation for iBeta Level 2 certification, Active and passive liveness for anti spoofing systems
Structure of the dataset with 18 Silicone masks
Source and collection methodology
The videos capture realistic spoofing conditions using different recording devices and variety of environments. Additionally, the dataset simulates common interactions like head movements and blinking, adding to its effectiveness in active liveness detection. The videos were shot using a front-facing (selfie) camera
Models of recording devices:
- IOS: iPhone 14, iPhone 14 Pro, iPhone 13 Pro
- Android:Â Galaxy S23, Xiaomi Redmi Note 12 Pro+, Galaxy A54, Pixel 7, Honor 70
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
See how this dataset was used to achieve iBeta Level 2 certification click here
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
“This dataset helped us achieve iBeta Level 2 compliance 30% faster!” – AI Security Lead
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
For two masks, video recordings are available from the back camera, capturing multiple angles (close-up, far, left, and right)
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
