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Printed 3D Masks Attacks Dataset

It is a diverse 3D mask attacks dataset containing over 3,800 videos of individuals wearing or holding 3D printed face masks, designed for training facial recognition and liveness detection models, with detailed metadata and realistic mask attacks to support robust anti-spoofing systems and image recognition tasks

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  • videos
    3,800+
  • devices
    5
  • iBeta
  • Liveness Detection
  • Computer Vision
  • Security

It is a diverse 3D mask attacks dataset containing over 3,800 videos of individuals wearing or holding 3D printed face masks, designed for training facial recognition and liveness detection models, with detailed metadata and realistic mask attacks to support robust anti-spoofing systems and image recognition tasks

Get in touch Download sample
  • iBeta
  • Liveness Detection
  • Computer Vision
  • Security
  • videos
    3,800+
  • devices
    5

Dataset Info

Characteristic Data
Description Videos of individuals wearing or holding 3D masks
Data types Video
Tasks Liveness Detection, Computer Vision, iBeta
Number of video 3 800+
Labeling Metadata (age, gender, ethnicity, devices)
Gender Male, Female
Number of attributes 31
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Statistics

Age of the unique people
Gender distribution
Ethnicity of the unique people

Technical
Characteristics

Characteristic Data
Video extension mp4, MOV
Video Resolutions Min = 1920х1080, Max = 3840х2160
Video duration 4 second
Number of background 9
Video overlap No more than 5%
Devices IOS, Android
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Biometrics & Security Systems

    Improving Facial Recognition Anti-Spoofing Models

    Printed 3D Masks Attacks Dataset provides a reliable foundation for developing anti-spoofing algorithms in facial recognition systems. The dataset contains high-resolution face images and recordings of mask attacks using 3D prints and replica face masks. By analyzing these samples, researchers can train image recognition models to differentiate between real faces and 3D mask attacks, improving the robustness of biometric authentication and identity verification technologies.

  • Artificial Intelligence & Machine Learning

    Training Models for Mask Attack Detection

    This dataset serves as high-quality training data for machine learning and deep learning systems designed to detect spoofing attempts. It comprises both genuine and printed 3D models, offering balanced samples for supervised learning. With structured data collection and labeled segmentation masks, it supports the development of algorithms capable of recognizing subtle texture and lighting differences between face masks and real human skin.

  • Digital Forensics & Fraud Prevention

    Enhancing Security in Identity Verification Systems

    In digital forensics, Printed 3D Masks Dataset aids experts in analyzing mask attacks used to deceive facial recognition systems. The dataset’s diversity of 3D prints and video masks dataset samples enables the testing of image segmentation techniques to isolate mask regions from authentic face images. Such data improves fraud detection systems, strengthening quality control in biometric verification and reducing false acceptances in high-security applications.

  • Computer Vision Research & Academia

    Image Recognition Models Against Spoofing Attempts

    For academic research and computer vision studies, this Printed 3D Masks Attacks Dataset offers valuable resources for testing and benchmarking image recognition models under spoofing conditions. The dataset consists of diverse samples of face masks, 3D models, and segmentation masks, allowing the evaluation of model accuracy in detecting artificial reproductions. It supports innovation in facial recognition, image segmentation, and deep learning architectures, contributing to more secure and trustworthy biometric technologies.

What is included in this dataset?
This dataset includes over 3,800 high-resolution videos of individuals wearing or holding 3D-printed face masks. Each video contains metadata on age, gender, ethnicity, and device type, captured in varying environments and lighting setups to ensure comprehensive facial recognition training.
Is it possible to request a custom dataset?
Yes. You can request a customized dataset tailored to your research needs, including specific mask materials, facial angles, lighting conditions, or device types. Unidata can also generate custom 3D mask attack videos with defined demographic attributes for enhanced testing in biometric and security applications.
How was the data collected?
All videos were collected by a verified Unidata partner under standardized conditions using iOS and Android devices. The recording process ensures diversity across backgrounds, resolutions, and camera types while maintaining strict privacy controls and data collection ethics.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are available for testing, while full datasets can be purchased for commercial use.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets are curated in full compliance with GDPR and relevant international data protection laws. The data is collected exclusively from legally authorized sources, ensuring that every video and metadata record adheres to ethical and lawful usage standards.
How are Unidata datasets stored?
All datasets are securely hosted on AWS cloud infrastructure, offering high availability and scalability for large-scale data handling. Unidata’s storage and management practices comply with ISO 27001 and ISO 27701 certifications, providing a secure, privacy-oriented framework for sensitive video and biometric data.
How long does it take to receive the dataset?
After you submit your request, Unidata will contact you to verify details and finalize documentation. Once agreements are signed and payment is confirmed, the dataset is delivered within 3–10 business days.
Is this a real-world dataset or synthetic data?
This is a real-world dataset composed of authentic videos of individuals interacting with 3D-printed face masks. The data was recorded using real devices in controlled environments to simulate realistic mask attacks, making it ideal for testing the resilience of facial recognition technologies.
Still have questions about using Unidata datasets? Read our user-guides

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