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Silicone Mask Attack Dataset

The dataset contains videos of human faces with silicone masks designed for presentation attacks, providing high-quality data for face recognition, anti-spoofing, and attack detection research, supporting the development of robust recognition systems, detection algorithms, and deep learning models against mask-based biometric attacks in compliance with iBeta Level 2 certification standards

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  • Videos
    6,500+
  • Devices
    5
Attack with the silicone mask and medical mask
  • iBeta
  • Liveness Detection
  • Computer Vision
  • Security
  • Facial Recognition
  • Anti-spoofing

The dataset contains videos of human faces with silicone masks designed for presentation attacks, providing high-quality data for face recognition, anti-spoofing, and attack detection research, supporting the development of robust recognition systems, detection algorithms, and deep learning models against mask-based biometric attacks in compliance with iBeta Level 2 certification standards

Get in touch Download sample
  • iBeta
  • Liveness Detection
  • Computer Vision
  • Security
  • Facial Recognition
  • Anti-spoofing
  • Videos
    6,500+
  • Devices
    5

Dataset Info

Characteristic Data
Description Videos of people in silicone masks training algorithms to detect biometric hacking attempts.
Data types Video
Tasks Face recognition, Computer Vision
Total number of videos 6,500
Total number of people 50
Labeling Only technical characteristics and metadata (age, gender, ethnicity)
Gender Male, Female
Ethnicity Caucasian (90%), African (10%)
Number of attributes 31
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Statistics

Number of devices of each type
Ethnicity in the dataset
Gender distribution

Technical
Characteristics

Characteristic Data
Video Extensions mp4
Video Resolutions Min = 1920х1080, Max = 3840х2160
Video Duration 1-2 second
Number of background 9
Video Overlap No more than 5%
Devices Mi10s, Google Pixel 4, Samsung Galaxy A03s, iPhone 11, iPhone SE 2 and etc.
Source and collection methodology: Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Biometrics and Security

    Developing silicone mask anti-spoofing systems

    Silicone Mask Attack Dataset provides realistic recordings of silicone masks, latex masks, and other presentation attacks. Recognition systems trained on this dataset can accurately detect spoofing attacks, improving biometric authentication and ensuring stronger protection in face recognition applications.

  • Financial Services

    Preventing identity fraud in KYC

    Banks and fintech companies use mask attack datasets to train recognition algorithms that detect 3D masks and other face presentation attacks. Learning models built on this data enhance liveness detection during onboarding, reducing risks of fraudulent verification through silicone masks or replay attacks.

  • AI and Machine Learning Research

    Benchmarking detection algorithms

    This dataset serves as training data for deep learning and image classification research. With diverse facial features and attack types, it allows researchers to compare recognition algorithms, test detection methods, and improve deep models for spoofing attack detection.

  • Forensics and Law Enforcement

    Enhancing facial recognition in investigations

    Law enforcement agencies can use Silicone Mask Attack Dataset to train recognition systems against synthetic dataset disguises. The database contains 3D masks, paper masks, and silicone masks, providing realistic cases for developing detection algorithms capable of identifying real faces hidden by advanced disguises during forensic analysis.

FAQs

How was Silicone Mask Attack Dataset collected?
The dataset was collected through crowdsourcing platforms, using devices such as Mi10s, Google Pixel 4, and iPhone 11. This method ensures a large collection of high-resolution facial images across various ages and backgrounds.
What are the technical characteristics of the dataset?
The dataset consists of MP4 videos with resolutions ranging from 1920×1080 to 3840×2160. Each video lasts 1–2 seconds, with up to 9 unique backgrounds, providing diverse training data for image classification and deep learning research.
Does the dataset contain both real and masked faces?
Yes, the dataset includes real faces and silicone masks to simulate mask attacks. This combination helps researchers build robust detection methods capable of distinguishing the facial features of synthetic datasets from authentic human faces.
How does this dataset support spoofing detection research?
By including silicone masks and varied spoofing attacks, the dataset enables testing of deep learning algorithms for anti-spoofing. It is particularly useful for evaluating recognition systems in high-security scenarios where presentation attacks are a concern.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets comply with GDPR and relevant international data privacy regulations. This dataset was curated from legally permissible sources to support ethical research in face presentation and biometric attack detection without compromising personal information or privacy standards.
How are Unidata datasets stored?
Unidata securely stores all datasets on AWS cloud infrastructure. Storage and management practices meet ISO 27001 and ISO 27701 standards, ensuring this Silicone Mask Dataset is protected within a privacy-focused environment optimized for reliability, scalability, and data integrity.
How long does it take to receive the dataset?
After submitting a request, Unidata contacts you to confirm details and complete required documentation. Once the agreement and payment are finalized, a dataset is usually delivered within 3–10 days.
Is this a real-world dataset or synthetic data?
This is a real-world dataset collected through crowdsourcing platforms featuring genuine presentation attacks using silicone masks. Unlike synthetic datasets or simulated print attacks, it captures authentic mask attacks on facial recognition systems, offering realistic training data for deep models and object detection algorithms.
Still have questions about using Unidata datasets? Read our user-guides

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