Update README.md

README.md

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 The dataset provides a robust foundation for achieving higher detection accuracy and advancing liveness detection methods, which are essential for preventing identity fraud and ensuring reliable biometric verification.
 
+# Frequently Asked Questions
 
+## What video resolution, format, and duration does the full 2D Masks with Eyeholes Attacks Dataset provide?
+
+The full anti-spoofing mask dataset is delivered in MP4 and MOV formats, with resolutions ranging from 1920 × 1080 up to 3840 × 2160. Each clip runs approximately 4 seconds, providing enough footage to capture a presentation attack attempt while keeping storage and preprocessing requirements manageable for real-time liveness detection pipelines.
+
+## Was the 2D mask attack data collected from real-world recordings?
+
+Yes. The collection was gathered through crowdsourcing platforms rather than generated entirely through synthetic video. The recordings therefore represent actual capture conditions involving people and printed 2D mask attacks.
+
+## How much background variation is included in the dataset?
+
+The videos were recorded against nine different backgrounds, introducing environmental variation beyond the mask attack itself.
 
 # 🌐 [UniData](https://unidata.pro/datasets/2d-masks/?utm_source=huggingface&utm_medium=referral&utm_campaign=2d-masks-pad-attacks) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects