Datasets:
Tasks:
Video Classification
Modalities:
Video
Size:
< 1K
Tags:
ibeta-level-1
ibeta-certification
presentation-attack-detection
liveness-detection
face-anti-spoofing
paper-mask-attack
License:
| license: cc-by-4.0 | |
| tags: | |
| - ibeta-level-1 | |
| - ibeta-certification | |
| - presentation-attack-detection | |
| - liveness-detection | |
| - face-anti-spoofing | |
| - paper-mask-attack | |
| - replay-attack | |
| - spoofing-dataset | |
| - biometric-security | |
| - pad-dataset | |
| - ibeta-dataset | |
| - anti-spoofing-dataset | |
| - face-liveness | |
| - presentation-attack | |
| - biometric-authentication | |
| task_categories: | |
| - video-classification | |
| # iBeta Level 1 Dataset: Facial Liveness Detection and Anti-Spoofing | |
| ## Full version of the dataset is available for commercial usage. Leave a request on our website [Axonlabs](https://axonlabs.pro/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to purchase the dataset 💰 | |
| ## For feedback and additional sample requests, please contact us! | |
| ## Dataset Description | |
| The iBeta Level 1 Paper & Replay Attacks Dataset offers a comprehensive collection of presentation attacks (PAD) tailored for iBeta Level 1 testing. Beyond paper-based masks and printouts, it includes a diverse set of replay attacks – photo/video replays on smartphone, and laptop displays under varying brightness levels, distances, and angles—to reflect real-world spoofing scenarios. Designed for researchers and developers working on liveness detection, this dataset provides broad coverage for training and validating anti-spoofing models, delivering end-to-end completeness for iBeta Level 1 | |
|  | |
| ## Key Features | |
| - **85+ paper-attack participants; 2,500+ selfie contributors for replay**: Engaged in the dataset creation, with a balanced representation of **Caucasian, Black, and Asian** ethnicities | |
| - **Video Capture**: Videos are captured on **iOS and Android phones**, featuring **multiple frames** and **approximately 10 seconds** of video per attack | |
| - **22,000+ Paper Mask Attacks**: Including a variety of attack types such as print and cutout paper masks, cylinder-based attacks to create a volume effect, and 3D masks with volume-based elements (e.g., nose) | |
| - **8k+ replay clips:** Combines different types of devices | |
| - **Active Liveness Testing**: Includes a **zoom-in and zoom-out phase** to simulate **active liveness detection** | |
| - **Variation in Attacks**: | |
| - Real-life selfies and videos from participants | |
| - **Print and Cutout Paper Attacks** | |
| - **Cylinder Attacks** to simulate volume effects | |
| - **3D Paper Masks** with additional volume elements like the nose and other facial features | |
| - Paper attacks on actors with **head and eye variations** | |
| - **Replay attacks:** photos/videos of participants replayed on smartphone screens (iOS/Android) and desktop monitors | |
| ## Potential Use Cases | |
| This dataset is ideal for training and evaluating models for: | |
| - **Liveness Detection**: Enabling researchers to distinguish between selfies and spoof attacks with high accuracy. | |
| - **iBeta Liveness Testing**: Preparing models for **iBeta** liveness testing, which requires precise spoof detection to meet certification standards. | |
| - **Anti-Spoofing**: Enhancing security in biometric systems by improving detection of paper mask spoofing techniques. | |
| - **Biometric Authentication**: Strengthening facial recognition systems to detect a variety of spoofing attempts. | |
| - **Machine Learning and Deep Learning**: Assisting researchers in developing robust liveness detection models for real-world applications. | |
| ## Keywords | |
| - iBeta Certifications | |
| - PAD Attacks | |
| - Presentation Attack Detection | |
| - Antispoofing | |
| - Liveness Detection | |
| - Spoof Detection | |
| - Facial Recognition | |
| - Biometric Authentication | |
| - Security Systems | |
| - AI Dataset | |
| - Paper Mask Attack Dataset | |
| - Anti-Spoofing Technology | |
| - Facial Biometrics | |
| - Machine Learning Dataset | |
| - Deep Learning | |
| ## Contact and Feedback | |
| We welcome your feedback! Feel free to reach out to us and share your experience with this dataset. If you're interested, you can also **receive additional samples for free**! 😊 | |
| Visit us at [**Axonlabs**](https://axonlabs.pro/?utm_source=hugging-face&utm_medium=cpc&utm_campaign=profile&utm_content=profile_link) to request a full version of the dataset for commercial usage. |