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Latex Mask Attacks Dataset

Latex Mask Attacks Dataset is a high-resolution collection of 11,100 videos of people wearing latex masks, created for training facial recognition and spoofing detection models. Featuring diverse facial features, metadata, and realistic presentation attacks, it supports fraud prevention research and iBeta Level 2 certification for robust biometric security systems.

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  • videos
    11,100+
  • devices
    5
  • iBeta
  • Liveness Detection
  • Computer Vision
  • Security
  • Facial Recognition
  • Anti-spoofing

Latex Mask Attacks Dataset is a high-resolution collection of 11,100 videos of people wearing latex masks, created for training facial recognition and spoofing detection models. Featuring diverse facial features, metadata, and realistic presentation attacks, it supports fraud prevention research and iBeta Level 2 certification for robust biometric security systems.

Get in touch Download sample
  • iBeta
  • Liveness Detection
  • Computer Vision
  • Security
  • Facial Recognition
  • Anti-spoofing
  • videos
    11,100+
  • devices
    5

Dataset Info

Characteristic Data
Description Video of people in latex masks training algorithms to detect biometric hacking attempts.
Data types Video
Tasks Face recognition, Computer Vision
Total number of files 11,100
Labeling Only technical characteristics and metadata (age, gender, ethnicity)
Gender Male, Female
Number of attributes 31
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Statistics

Devices in the dataset
Gender distribution

Technical
Characteristics

Characteristic Data
Video extension mp4
Video Resolutions Min = 1920х1080, Max = 3840х2160
Video duration 1-2 second
Number of background 9
Video overlap No more than 5%
Devices IOS, Android
Source and collection methodology. Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Biometric Security

    Strengthening Spoofing Detection Systems

    Latex Mask Attacks Dataset provides 11,100 high-resolution videos of human faces with latex masks, helping researchers build stronger spoofing detection and attack detection systems. This dataset supports the training of recognition algorithms that distinguish between real faces and 3D masks, reducing risks from advanced presentation attacks.

  • Financial Services

    Enhancing Fraud Prevention in Authentication

    Banks and fintech platforms use such datasets to improve facial recognition during identity verification and KYC checks. With datasets containing face images and spoofing attacks, institutions can train deep models to detect mask attacks and ensure secure, fraud-resistant systems for financial transactions and digital onboarding.

  • Healthcare & Access Control

    Securing Medical Data and Restricted Areas

    Hospitals and laboratories benefit from this dataset by developing training data for recognition systems that protect medical images and records. With videos of latex masks simulating face presentation attacks, developers can build detection algorithms that prevent unauthorized entry and safeguard sensitive health information against biometric spoofing.

  • AI Research & Certification

    Advancing Deep Learning and iBeta Standards

    For AI researchers, Latex Mask Attacks Dataset, comprising videos of latex mask attacks, offers valuable material for deep learning models in facial recognition. By analyzing facial features of mask attacks, teams can create learning algorithms that improve image classification and prepare systems for compliance with iBeta Level 2 certification and other security benchmarks.

FAQs

What types of annotations are provided?
The dataset provides metadata annotations, including demographic details and technical recording attributes. These labels help train deep learning models for identifying real faces vs. latex masks.
How was the dataset collected?
Videos were captured with binocular RGB and infrared cameras on iOS and Android devices. Recordings span nine different backgrounds with resolutions ranging from 1920×1080 to 3840×2160, ensuring robust training data for detection algorithms.
What is the average duration and quality of videos?
Each video is 1–2 seconds long, designed for quick facial recognition testing. The dataset includes high-resolution footage suitable for object detection, image classification, and biometric attack research.
How does this dataset differ from other spoofing datasets?
Unlike datasets focusing on printed photos or replay attacks, this collection highlights latex masks and 3D presentation attacks. It provides more advanced spoofing scenarios, making it essential for next-generation liveness detection systems.
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