Datasets:
Languages:
English
Size:
< 1K
Tags:
silicone mask
silicone mask attack
3d mask
biometric security
attack detection
liveness detection
License:
| 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 | |
| - face-anti-spoofing | |
| - face-recognition | |
| - presentation-attack-detection | |
| - pad | |
| - ibeta-level-2 | |
| - iso-30107-3 | |
| - biometric-anti-spoofing | |
| size_categories: | |
| - 10K<n<100K | |
| pretty_name: Silicone Mask Dataset | |
| # Silicone Mask Dataset — 12,500+ Videos for Face Anti-Spoofing & Liveness Detection | |
| A silicone mask presentation attack dataset for **face anti-spoofing**, **liveness detection**, and **biometric face recognition** systems. The dataset contains 12,500+ attack videos featuring 18 hyper-realistic silicone masks, designed for training and benchmarking **presentation attack detection (PAD)** models. Aligned with the **ISO/IEC 30107-3** standard for biometric anti-spoofing and suitable for **iBeta Level 2** PAD certification preparation | |
| Covers 8 devices, 5 shooting angles, ~40 attribute combinations (wigs, glasses, beards), and diverse real-world environments — offices, apartments, and outdoor locations. | |
| ## What Is a Silicone Mask Attack? | |
| A silicone mask attack is a 3D presentation attack vector against face recognition and liveness detection systems. Unlike 2D paper masks or photo/video replay attacks, high-realism silicone masks reproduce | |
| facial geometry, skin texture, and even subtle reflectance properties, defeating many traditional anti-spoofing techniques that rely on 2D texture cues, depth analysis, or basic motion-based liveness | |
| detection | |
| Silicone masks are among the hardest presentation attack vectors to detect, which is why they are the primary attack class tested under iBeta Level 2 certification (ISO/IEC 30107-3). Robust face anti-spoofing systems must distinguish silicone masks from genuine faces under realistic capture conditions: varied lighting, distances, devices, and angles | |
| ## Key Features | |
| - **3D mask attacks only** — purely high-fidelity silicone mask presentations, not photos or screen replays | |
| - **Scale** — 12,500+ 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. | |
| ## Academic Baseline Reference | |
| Researchers familiar with face anti-spoofing literature may know the [**Idiap CSMAD** (Custom Silicone Mask Attack Database)](http://idiap.ch/en/scientific-research/data/csmad) and its extension **XCSMAD** - the canonical academic benchmarks for silicone | |
| mask anti-spoofing research, published by the Idiap Research Institute. This commercial dataset extends that line of work with significantly more silicone masks (18 vs CSMAD's 14), broader demographic and | |
| accessory variation (40+ attribute combinations including hairstyles, glasses, wigs, and beards), and modern smartphone capture conditions, designed for production face recognition and liveness detection systems rather than research benchmarks alone | |
| ## Related Datasets by Axon Labs | |
| - [Latex Mask Attack Dataset](https://huggingface.co/datasets/AxonData/Latex_Mask_dataset) | |
| — 4,000+ videos with latex masks for additional 3D mask coverage | |
| - [Advanced 3D Paper Mask Attack Dataset](https://huggingface.co/datasets/AxonData/3D_paper_mask_attack_dataset_for_Liveness) | |
| — volumetric paper-based attacks (cylinder, structural elements) | |
| - [Advanced Paper Attacks Dataset](https://huggingface.co/datasets/AxonData/face-anti-spoofing-advanced-paper-attacks) | |
| — eyeholes masks, cutouts, wrapped paper attacks | |
| - [Display Replay Attacks Dataset](https://huggingface.co/datasets/AxonData/Display_replay_attacks) | |
| — smartphone, laptop, and monitor replay attacks | |
| - [iBeta Level 2 Full Dataset](https://huggingface.co/datasets/AxonData/iBeta-Level-2-Certification-Dataset) | |
| — combined attack videos for full Level 2 certification preparation | |
| - [iBeta Level 3 Dataset](https://huggingface.co/datasets/AxonData/ibeta-level-3-dataset) | |
| — high-fidelity rubber and 3D resin masks for the strictest L3 testing |