Datasets:
Languages:
English
Size:
< 1K
Tags:
silicone mask
silicone mask attack
3d mask
biometric security
attack detection
liveness detection
License:
File size: 3,721 Bytes
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license: cc-by-4.0
task_categories:
- image-feature-extraction
- image-classification
- video-classification
language:
- en
tags:
- silicone mask
- silicone mask attack
- 3d mask
- biometric security
- attack detection
- liveness detection
- anti-spoofing
- biometrics
- facial recognition
- iBeta certification
- PAD attack
- security
- ibeta
- face recognition
size_categories:
- 10K<n<100K
---
# Silicone Mask Attack Dataset — 10,000+ Videos for Anti-Spoofing
Anti-spoofing dataset with 10,000+ attack videos featuring 18 hyper-realistic silicone masks. Designed for training liveness detection and presentation attack detection (PAD) models targeting **iBeta Level 2** certification.
Covers 8 devices, 5 shooting angles, ~40 attribute combinations (wigs, glasses, beards), and diverse real-world environments — offices, apartments, and outdoor locations.
## Key Features
- **3D mask attacks only** — purely high-fidelity silicone mask presentations, not photos or screen replays
- **Scale** — 10,000+ videos provide sufficient data for deep learning approaches without heavy augmentation
- **Demographic diversity** — 18 masks spanning male/female, Caucasian/Asian appearances
- **Real-world variability** — recorded in offices, apartments, and outdoor scenes, not just lab conditions
Full dataset is available for commercial licensing — [request access on Axon Labs website](https://axonlab.ai/). This repository contains a preview sample.
Successfull Spoofing attack on a Liveness test by [Duobango ](https://www.doubango.org/webapps/face-liveness/)
## Recording Conditions
**Capture Devices (8 models)**
iPhone 14, iPhone 14 Pro, iPhone 13 Pro, Samsung Galaxy S23, Samsung Galaxy A54, Google Pixel 7, Xiaomi Redmi Note 12 Pro+, Honor 70
**Shooting Angles (5 views)**
Front selfie, back camera close-up, back camera far, left side, right side
**Attribute Variations (~40 combinations)**
Each mask is recorded with combinations of wigs, glasses, beards, and different hairstyles — simulating how real attackers modify mask appearance to bypass detection.
**Active Liveness Challenges**
Videos include natural head movements and blinking to specifically test active liveness detection pipelines that rely on motion-based cues.
## Intended Use Cases
**Training PAD classifiers** — Use as attack samples paired with your genuine (bona fide) data to train binary or multi-class anti-spoofing models.
**Benchmarking liveness detection** — Evaluate existing models against high-quality 3D mask attacks to identify failure modes before iBeta testing.
**Multi-modal fusion research** — Combine with depth, IR, or thermal data to study cross-modal attack detection strategies.
**Adversarial robustness testing** — The ~40 attribute combinations (glasses, wigs, beards) let you test model robustness against disguise variations.
## Related Datasets by Axon Labs
- [Latex Mask Attack Dataset](https://huggingface.co/datasets/AxonData/Latex_Mask_dataset) — 4,000 videos with latex masks
- [Advanced Paper Mask Attack Dataset](https://huggingface.co/datasets/AxonData/face-anti-spoofing-advanced-paper-attacks) — 2,500 videos with wrapped 3D paper masks
- [iBeta Level 2 Full Dataset](https://huggingface.co/datasets/AxonData/iBeta-Level-2-Certification-Dataset) — 25,000+ combined attack videos |