| --- |
| 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 |
| --- |
| # Liveness Detection Dataset: iBeta level 2 advanced mask attacks (5 K videos) |
| Anti-Spoofing Paper Attacks iBeta 2 - 5,000 videos |
| 4 different attack types, advanced paper attacks for Liveness Detection |
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| ## 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 💰 |
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| ## 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 |
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| ## 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. |
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| ## 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. |
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| ## Related Datasets |
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| - [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 |
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| Keywords: iBeta certification, PAD attacks, Presentation Attack Detection, Antispoofing, Facial Biometrics, Biometric Authentication, Security Systems, Machine Learning Dataset |
