Introduction
This Dataset is a specialized face anti-spoofing dataset featuring personalized rubber mask attacks targeting facial recognition systems and biometric authentication. This unique dataset employs custom-manufactured rubber face masks designed to replicate specific individuals’ facial features with high precision
What sets Rubber Mask Attack Dataset apart from conventional PAD datasets is its dual-recording methodology — capturing both authentic face videos and corresponding rubber mask attack videos of the same target individuals
Dataset summary
- Dataset Size: ~2k videos shoot on 6 IDs
- The presence of videos of original faces (real people) from which masks were made
- Active Liveness Features: Includes zoom-in, zoom-out, and right/left rotation, head tilt to enhance training scenarios
- Attributes: Different hairstyles and accessories to enhance diversity
- Variability: indoor locations with different types of lighting
- Main Applications: Preparation for iBeta Level 2 certification, active and passive liveness for anti spoofing systems
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
Other similar datasets for iBeta Level 2
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
- 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
Have a question?
We collect data from our internal team. All information is further verified by our specialists
Once your enquiry has been sent, we will contact you to discuss the details and complete the necessary paperwork. The timing of receiving the dataset depends on the specific request and additional requirements
Our unique selling point is to provide legally clean datasets to our customers. We obtain the consent from all the participants to use their data for AI model development. We are able to provide comprensive reporting on the licensing, data collection and privacy compliance of our datasets. Although there seems to be a diverse response to how to control AI development and deployment, we are able to service global customers seeking to launch global AI products.
The dataset follows iBeta testing protocols and includes diverse attack scenarios that mirror real-world spoofing attempts. It covers both passive and active liveness testing requirements with proper demographic representation and standardized capture conditions essential for certification preparation
The price depends on your specific requirements. Please submit a request to receive a free consultation
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Didn't find what you were looking for?
Our collection includes many datasets for various requests
iBeta Level 1 Paper
– 22,000+ videos
– 80+ participants
– zoom in and
zoom out
Replay Display attacks
– 5,000+ videos
– 1,000+ participants
– Balanced mix of genders and ethnicities
Photo Print Dataset
– 7000+ videos.
– 10-20 second each video
– Mix of genders
Silicone Mask Dataset
– 10 000+ videos
– 18 Silicone Masks
– iBeta Level 2
Liveness Detection
