iBeta Level 2 Dataset
There are >25K videos tailored for iBeta level 2 certification from 150+ IDs
Check samples on Kaggle
Introduction
iBeta Level 2 PAD is a compact training dataset for liveness detection and anti-spoofing focused on 3D mask attacks and active liveness. It includes 25k+ short multi-frame videos captured on diverse iOS and Android devices, subjects, and conditions. Covered attack types: silicone, latex, wrapped 3D paper, advanced paper, and cloth 3D masks. Ideal for training/validation and pre-certification experiments targeting iBeta Level 2
iBeta Level 2 dataset summary
Dataset size: 25,000+ short multi-frame videos from 150+ IDs focused on Level-2 PAD; diverse subjects across genders and ethnicities
Attack coverage: Silicone, Latex, Wrapped 3D Paper, Advanced Paper, Cloth 3D face masks
Active liveness content: Guided zoom-in / zoom-out, subtle micro-movements, natural head turns and off-axis looks; mask approach/retreat sequences
Capture & devices: Every attack recorded on both iOS and Android phones; varied resolutions/frame rates suitable for ML training
Variability: Indoor/outdoor scenes, multiple lighting levels, different backgrounds, distances, and view angles; accessories and appearance changes to increase diversity
Source and collection methodology
We captured realistic iBeta Level 2 spoofing scenarios with front-facing cameras across varied people, environments, and devices. Each clip follows an active liveness script (zoom-in/zoom-out, natural head turns/blinks) and lasts ~10 seconds
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
Capture protocol
3D mask attacks
Guided zoom phases
Multiple distances
Environments & lighting
Indoor (offices, home settings) and outdoor scenes
Three lighting levels: low, medium, bright; mixed backgrounds
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
How companies achieved iBeta with us
This dataset is ideal for teams focused on liveness detection and PAD model training. It’s especially valuable for developers preparing their models for iBeta certification, as it includes a comprehensive set of spoofing scenarios required for level 1 testing
How industry leaders achieve superior liveness detection with our dataset
Technology company from Vietnam: iBeta Level 2 success
A Vietnam-based AI/Big Data firm coached by Axon Labs passed iBeta PAD Level 2 on the first try with 0% successful spoofs; the solution claims 99.9% face-recognition accuracy
Fintech Company from Brazil: iBeta Level 1 Success
One of the largest fintechs in Brazil approached us to prepare an active biometric authentication system for iBeta Level 1 certification
Digital Bank from Vietnam: iBeta Level 2 success
Digital bank from Vietnam asked Axon Labs to prepare its anti-spoofing model in order to pass iBeta Level 2 on the first attempt, and the goal was achieved
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
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 Dataset
– 22,000+ videos
– 80+ participants
– zoom in and
zoom out
iBeta Level 2 Dataset
– 25 000+ videos
– 3D masks
– iBeta Level 2
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
