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

The 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 realistic presentation attacks, this dataset significantly supports advancements in detecting biometric spoofing

Dataset summary

Dataset Size

~10,000 videos demonstrating various spoofing; 18 high-detail silicone masks

Active Liveness Features

Includes natural head movements and blinking to enhance training scenarios

Variability in Attributes

Includes different hairstyles, glasses, and clothing to enhance data diversity

Main Applications

Active and passive liveness detection systems, preparation for iBeta Level 2 certification to detect spoofing attacks

Source and collection methodology

The videos capture realistic spoofing conditions using different recording devices to represent varied environments and devices. Additionally, the dataset simulates common interactions like head movements and blinking, adding to its effectiveness in active liveness detection

Models of recording devices:

– iPhone 14

– iPhone 14 Pro

– iPhone 13 Pro

– Galaxy S23

– Xiaomi Redmi Note 12 Pro+

– Galaxy A54

– Pixel 7

– Honor 70

Use cases and applications

iBeta Level 2 Certification Compliance

Allows pre-certification testing to assess system performance before submission
Helps fine-tune and retrain models that fail specific certification tests

Liveness Detection Models

Used for training and validation of anti-spoofing models
Suitable for developing custom solutions in biometric security
Enables testing of existing algorithms and identification of their vulnerabilities against spoofing attacks

See how this dataset help to achieve iBeta Level 2 click here

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

Dataset details

The Silicone Mask Attack Dataset is designed to address security challenges in liveness detection systems through 3D silicone mask attacks. These presentation attacks are key for testing Liveness Detection at iBeta level 2 certification. The dataset significantly enhances the capabilities of liveness detection models

To ensure high variety, 8 silicone masks were created and used with different hair styles, clothes, face masks and glasses to shoot various attacks. Each attack is approximately 8 seconds long and includes head movements as well as blinking. The dataset is suited for both Active and Passive liveness detection

Dataset description

8 unique silicone masks (male and female, Caucasian and Asian ethnicity)
Roughly 7,000 videos with detailed spoofing detection scenarios
Head movements and blinking are included in the dataset for active liveness detection
Variations of shooitng angles, 5 angles in total: Selfie, back camera far / close, two side shots
Three different devices on iOS and Android (Modern iPhone, Xiaomi, Samsung Galaxy)
Diverse lighting and background settings to ensure robustness
Advanced set of attributes including different hair styles, glasses, and clothes

Best used for:

Potential customisation options:

Download information

A sample version of this dataset is available on Kaggle. Leave a request for additional samples in the form below

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