Update README.md

README.md

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 ## Dataset Description
 
-The **iBeta Level 1 Certification Dataset** focuses on **paper mask attacks** tested during **iBeta Level 1** **Presentation Attack Detection (PAD)**. This dataset includes multiple variations of paper mask attacks for training AI models to distinguish between real and spoofed facial data, and it is tailored to meet the requirements for iBeta certifications.
+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
 
 ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F8a6996cd222975b552b2afb91bd977bf%2FIbeta-paper-attacks-1_page-0001-e1741801105376.jpg?generation=1747384579351225&alt=media)
 
 ## Key Features
-- **80+ Participants**: 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).
-- **Active Liveness Testing**: Includes a **zoom-in and zoom-out phase** to simulate **active liveness detection**.
+- **80+ 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**.
+  - 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: