AxonData/Silicone-Mask-Attack-Dataset

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. This repository contains a preview sample.

Successfull Spoofing attack on a Liveness test by Duobango

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 — 4,000 videos with latex masks
• Advanced Paper Mask Attack Dataset — 2,500 videos with wrapped 3D paper masks
• iBeta Level 2 Full Dataset — 25,000+ combined attack videos