Dataset Summary
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Parameter
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Value
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Volume
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12,500+ videos with 18 high-detail silicone masks
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Coverage
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Silicone mask presentation attacks with ~40 attribute combinations for iBeta Level 2 PAD
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Demographics
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18 mask identities across mixed gender and adult age range
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Devices
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iOS and Android phones (8 device models)
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Conditions
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4 office spaces, 4 home apartments, 2 outdoor locations, 3 lighting levels (low, medium, bright)
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Introduction
The Silicone Mask PAD Dataset provides 12,500+ attack videos across 18 high-detail silicone masks, designed for training and validating liveness detection models against 3D silicone mask presentation attacks, the primary attack vector tested in iBeta Level 2 certification. Each mask is recorded with ~40 attribute combinations (hairstyles, glasses, wigs, beards) across 10 distinct indoor and outdoor locations under three lighting levels. iBeta Level 2 certification tests biometric systems against ISO/IEC 30107-3 – the international standard for biometric presentation attack detection
Dataset Features
- High-detail silicone construction: All 18 masks professionally manufactured for realistic skin texture
- Active liveness sequences: Natural head movements and blinking per video
- Cross-device per mask: Every mask recorded on all 8 device models
- Indoor/outdoor split: Controlled office and home scenes plus uncontrolled outdoor capture
Structure of the Dataset, % (N = 18 IDs)
Structure of the dataset with 18 Silicone masks
Source and collection methodology
We captured realistic silicone mask attacks with front-facing cameras across 18 high-detail masks, varied environments, and attribute combinations. Each clip follows an active liveness script (natural head movements, blinking) and covers multiple lighting scenarios. Data collection complies with GDPR Article 9 for the processing of biometric data.
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
Real-World Validation: Open Liveness Model Stress Test
To demonstrate the practical value of this dataset, we tested its samples against Doubango’s open-source face liveness model – a publicly available liveness detection SDK used as a reference implementation by anti-spoofing researchers and developers.
Key result: Doubango’s liveness classifier rated the partial paper mask attack below as 99.97% genuine
Use cases and applications
Face Anti-Spoofing & PAD: Train presentation attack detection models to identify partial paper overlay attacks – a blind spot for systems trained only on full-face prints, replay attacks, or 3D masks
Liveness Detection: Improve liveness detection robustness by exposing models to attacks where real facial movement co-exists with printed fragments, challenging motion-based and texture-based detectors simultaneously
- iBeta Certification Preparation: Test your liveness system against realistic 2D partial attacks before submitting to iBeta Level 1 or Level 2 certification
iBeta Success Stories
Datasets like this contributed to 21% of companies that passed iBeta certification in 2025 – all Axon Labs clients
From Failed Attempt to Success: UK Fintech Passed iBeta 2
UK fintech invested in datasets from another vendor for iBeta 2 and failed. The gap analysis revealed critical missing attack vectors. They needed a solution fast
Double Success: E-KYC Startup Achieves iBeta 1 & 2
A US-based e-KYC startup needed to prove their facial recognition technology met the highest industry security standards. Their ambitious goal: pass iBeta 1 and 2 Certification
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
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)
Other Datasets for iBeta 2:
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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