---
license: cc-by-nc-4.0
task_categories:
  - video-classification
  - image-classification
tags:
  - face-anti-spoofing
  - liveness-detection
  - print-attack
  - cardboard-mask
  - presentation-attack-detection
  - face-spoofing
  - mask-attack
  - 2d-mask
  - biometrics
  - PAD
  - computer-vision
size_categories:
  - 1K<n<10K
language:
  - en
---
# Cardboard Mask Face Anti-Spoofing Dataset

3,000 videos from 50 participants of print cardboard mask attacks with real accessories - wigs, hats, glasses. Recorded on iPhone 12, iPhone 14 Pro, and Samsung S23. Built for training and evaluating **face anti-spoofing**, **liveness detection**, and **presentation attack detection** systems

> **Full dataset for commercial use** — request a license at [axonlab.ai](https://axonlab.ai/?utm_source=hugging-face&utm_medium=referral&utm_campaign=cardboard-mask-dataset)

## Why Accessory-Enhanced Masks Are a Harder Problem

Standard **print attack** datasets use flat photos held in front of a camera. Cardboard mask attacks with real accessories are fundamentally different:

- **Real accessories** (wigs, hats, glasses) add volume and conceal mask edges
- **Rigid cardboard** holds its shape when worn, unlike paper that bends and warps
- **Mixed signals** - real hair, clothing, and movement surround a printed face, confusing texture-based detectors

This makes accessory-enhanced cardboard masks a blind spot for PAD systems trained only on flat print or replay attacks

## Dataset Specifications

- **3,000 videos** from **50 unique participants**
- **Multi-device**: iPhone 12, iPhone 14 Pro, Samsung S23
- **Active liveness features**: zoom-in/out, natural head movements, blinking
- **Varied environments**: diverse real-world backgrounds and lighting conditions
- **Accessories per attack**: wigs (various styles and colors), hats, caps, glasses, sunglasses

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F139b4831c376d8f79f32aeaba7fe3aad%2Fdataset_preview_cardboard.png?generation=1775148797949084&alt=media)

## Applications

- **Face anti-spoofing** - train PAD models to detect accessory-enhanced print mask attacks
- **Liveness detection** - build systems robust to attacks combining printed faces with real-world objects
- **iBeta certification preparation** - test against advanced 2D presentation attacks before Level 1/2 submission

## Need More Data?

This dataset is a ready-made sample. We offer **custom data collection** for cardboard mask attacks tailored to your requirements, including larger participant pools, additional devices, specific demographic distributions, and custom accessory configurations

Contact us at [axonlab.ai](https://axonlab.ai/?utm_source=hugging-face&utm_medium=referral&utm_campaign=cardboard-mask-dataset-custom) to discuss your project.