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README.md

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-license: cc-by-nc-4.0
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+license: cc-by-nc-4.0
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+Anti-Spoofing Paper Attacks iBeta 2 - 5,000 videos
+4 different attack types, advanced paper attacks for Liveness Detection
+
+# Liveness Detection Dataset: iBeta level 2 advanced mask attacks (5 K videos)
+
+## 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
+
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F94274e167f4b040d15c735f1dc7ed6f9%2FFrame%20102.png?generation=1752471351405262&alt=media)
+
+## 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.
+
+
+## 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.
+
+Keywords: iBeta certification, PAD attacks, Presentation Attack Detection, Antispoofing, Facial Biometrics, Biometric Authentication, Security Systems, Machine Learning Dataset