---
license: cc-by-nc-4.0
tags:
- liveness detection
- anti-spoofing
- biometrics
- facial recognition
- machine learning
- deep learning
- AI
- paper mask attack
- iBeta certification
- PAD attack
- security
- ibeta
- face recognition
- pad
- authentication
- fraud
task_categories:
- video-classification
pretty_name: Paper Attack Dataset
---
# Liveness Detection Dataset: iBeta level 2 advanced mask attacks (5 K videos)
This advanced paper mask attack dataset focuses on **complex paper-based presentation attacks** for **face anti-spoofing**, **liveness detection**, and **biometric face recognition** systems. The dataset contains **5,000 videos** across **7 distinct attack scenarios** - including printed-attribute photos, cut-out photo masks, photo masks worn by actors with real accessories (wigs, hats, glasses)

Recorded from **25 participants** across **iOS and Android devices** with balanced gender mix and multi-ethnic representation (Caucasian, Black, Asian, Latinx). Active-liveness phases (fixed, zoom-in, zoom-out) are included for robust **presentation attack detection (PAD)** model training. Aligned with the **ISO/IEC 30107-3** standard and designed for **iBeta Level 2** certification preparation

## Full version of dataset is availible for commercial usage - leave a request on our website [Axonlabs ](https://axonlab.ai/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link)to purchase the dataset 💰

## Dataset Description
- **25 participants** recorded under signed consent
- **Dual-device capture:** iOS / Android phones 
- **Diverse representation:** balanced gender mix and broad ethnicity coverage (Caucasian, Black, Asian, Latinx)
- **5 000 videos**
- **Active-liveness phases:** fixed, zoom-in, zoom-out

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F94274e167f4b040d15c735f1dc7ed6f9%2FFrame%20102.png?generation=1752471351405262&alt=media)

## Types of Presentation Attacks (paper masks)
- **1. Printed attributes on photo** – a flat facial photo with accessories (e.g., glasses, hat) printed together with the face.
- **2. Cut-out attributes in photo** – a flat facial photo cut to the shape of the face.
- **3. External attributes on top of photo** – a flat facial photo with real accessories (glasses, cap, etc.) attached on top.
- **4. Photo mask on actor + external attributes** – a full-size photo fixed to an actor’s face; real items such as a hood or wig are added.
- **5. Photo mask on actor, printed attributes** – a fixed photo that already contains additional printed attributes.
- **6. Photo mask on actor with eye holes + external attributes** – eye openings are cut in the photo; the actor blinks through them while wearing real wig/clothing.
- **7. Photo mask with printed attributes and eye holes** – combines printed accessories on the photo with the actor’s live eyes visible through cut-outs.


## Potential Use Cases
- **Liveness detection R&D:** train / benchmark algorithms that separate selfies from 3D mask spoofs with high accuracy.
- **iBeta level 2 pre-certification:** stress-test PAD models against high-realism 3D mask scenarios before formal audits.
- **Cross-material studies:** analyse generalisation gaps between silicone, latex, paper and textile attacks for robust deployment.

## Related Datasets

- [3D Paper Mask Attacks for Liveness](https://huggingface.co/datasets/AxonData/3D_paper_mask_attack_dataset_for_Liveness) — volumetric 3D paper mask attacks
- [Display Replay Attacks](https://huggingface.co/datasets/AxonData/Display_replay_attacks) — screen replay attacks
- [Print Attack Dataset](https://huggingface.co/datasets/AxonData/anti_spoofing_dataset_print_attack) — photo print attacks
- [iBeta Level 1 Certification Dataset](https://huggingface.co/datasets/AxonData/ibeta-level-1-certification) — iBeta L1 attack set


Keywords: iBeta certification, PAD attacks, Presentation Attack Detection, Antispoofing, Facial Biometrics, Biometric Authentication, Security Systems, Machine Learning Dataset