Silicone Mask dataset

There are >10K videos from 18 Silicone mask attacks tailored

for iBeta level 2 certification

Check samples on Kaggle

Successfull Spoofing attack on a Liveness test by Doubango

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

Samples of Indoor video attacks:

Samples of Outdoor video attacks:

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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: Galaxy S23, Xiaomi Redmi Note 12 Pro+, Galaxy A54, Pixel 7, Honor 70

Use cases and applications

iBeta Level 2 Certification Compliance:

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:

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 solutions

“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

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