Wrapped 3D Attacks

There are >2K videos from 20 mask attacks tailored

for iBeta level 2 certification

Check samples on Kaggle

Successfull Spoofing attack on a Liveness test by Doubango

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: 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 systems

Samples of video attacks:

Get access now

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

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:

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

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

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 dataset