Update README.md

README.md

@@ -52,6 +52,10 @@ Robust face anti-spoofing systems must detect print attacks reliably under varie
 - No visible image borders during the Zoom-in phase
 - Paper attacks conducted on flat photos with a straight view on the camera (not bent or skewed)
 
+## Academic Baseline Reference
+
+The canonical academic benchmark for print attack anti-spoofing research is the **Idiap Print-Attack Database** ([idiap.ch/en/scientific-research/data/printattack](https://www.idiap.ch/en/scientific-research/data/printattack)), published by the Idiap Research Institute as one of the foundational datasets in face anti-spoofing literature. This commercial dataset extends Idiap's research line with significantly more participants (3,000+ vs Idiap's 50), broader demographic representation, NIST-FATE-compliant zoom-in effects, and modern smartphone capture conditions, designed for production face recognition and liveness detection systems rather than research benchmarks alone
+
 ## Potential Use Cases:
 Liveness detection: This dataset is ideal for training and evaluating liveness detection models, enabling researchers to distinguish between selfies and photo print attacks with high accuracy