Dataset Summary
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Parameter
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Value
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|---|---|
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Volume
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11,000+ videos with 80+ unique mask designs
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Coverage
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Cloth 3D mask attacks for iBeta Level 2 PAD certification
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Demographics
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Adults, mixed gender, multi-ethnic
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Devices
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iOS and Android phones (5 models): iPhone 14 Pro, iPhone 12, Samsung S23, Galaxy A54, Pixel 7
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Conditions
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Office and apartment capture across 50+ backgrounds, with natural, artificial, directional, and low-light conditions
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Introduction
The Cloth 3D Mask Dataset provides 11,000+ attack videos using low-cost textile masks as an accessible alternative to expensive silicone and latex mask datasets, while still meeting the presentation attack requirements for iBeta Level 2 certification. The masks are thin elastic fabric worn like a balaclava, with photorealistic facial images that conform to each wearer’s head shape through textile elasticity. iBeta certification tests biometric systems against ISO/IEC 30107-3 – the international standard for biometric presentation attack detection
Dataset Features
- Elastic textile construction: Thin fabric masks with textile elasticity for head-conforming fit
- Full-head coverage: Masks worn like a balaclava for complete 3D face coverage
- Personalized print per mask: Each mask printed with the participant’s photorealistic face image
- 30–50 attribute combinations per mask: Different hairstyles and accessories across sessions
- Active liveness sequences: Head movements, and zoom-in/zoom-out per video
Simplified Alternative to Silicone and Latex Masks
Modern Face Recognition and Liveness Detection systems remain vulnerable to 3D presentation attacks using silicone masks and latex face masks. However, these premium spoofing materials require expensive manufacturing processes and specialized equipment, limiting their accessibility
Textile 3D Face Mask Attack Dataset addresses this gap by providing cost-effective presentation attack scenarios using elastic textile masks — a budget-friendly alternative to existing premium datasets:
Elastic textile technology enables partial facial geometry mimicking through material stretching and contour adaptation, while maintaining simple DIY manufacturing and widespread material availability
Comparison to Other 3D Mask Datasets
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Feature
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Textile Mask
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Latex Mask
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Silicone Mask
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|---|---|---|---|
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Material
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Textile
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Latex
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Silicone
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Cost of mask
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Low
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Average
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High
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Face-hugging
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Average
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High
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High
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Spoof Success Demonstration
This screenshot demonstrates that our cloth mask attack successfully bypasses a face liveness detection model from KBY-AI — a widely used commercial solution known for its high performance in biometric verification systems. The model classified the spoofed face as Real with a high confidence score (0.89), confirming that even top-tier PAD systems can be vulnerable to this type of attack
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
How companies achieved iBeta with us
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
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 3D Cloth Mask 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?
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iBeta Level 2 Dataset
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iBeta Level 3 Dataset
– 10,000+ videos
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Display Replay Dataset for Liveness Detection
– 9,000+ videos
– 6,500+ participants
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