Update README.md

README.md

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 ## Multi-Mask PAD Attacks: Cardboard, Textile Mesh, Silicone
 
-Dataset for PAD/liveness detection with three classes of presentation attacks: cardboard masks with cut-out eyes; elastic textile mesh masks with full-color face printing; full-size silicone 3D masks. Mask types were selected based on real-world attack scenarios
+Dataset for PAD/liveness detection with three classes of presentation attacks: cardboard masks with cut-out eyes; elastic textile mesh masks with full-color face printing; full-size latex 3D masks. Mask types were selected based on real-world attack scenarios
 
 # The key value is high variety: 
 different actors, a large number of individual masks, various lighting (indoor/outdoor, natural/artificial light), backgrounds and locations, shooting angles and distances, different cameras/lenses, accessories (glasses/headwear/beards). This range reduces retraining on specific mask textures and increases generalizability in passive liveness detection and attack type recognition
@@ -19,7 +19,7 @@ different actors, a large number of individual masks, various lighting (indoor/o
 Flat face print; eyes cut out to allow for real eye movement. Flat image with hard edges around the outline
 - Textile — thin elastic mesh mask with face print
 Nylon/elastic fabric mask; full-color face print, fabric fits snugly around the head and partially replicates the 3D shape
-- Silicone — volume 3D silicone masks
+- Latex — volume 3D latex masks
 Full-size realistic masks with pronounced “skin” texture. Sometimes they have cut-out eyes and are complemented by external attributes
 
 # Why is this combination of attacks optimal?