A paired-sampling contrastive framework unifies physical and digital face attack detection, achieving a 2.10% ACER on the 6th Face Anti-Spoofing Challenge.
Forgery-aware adaptive learning with vision transformer for generalized face forgery detection.IEEE Transactions on Circuits and Systems for Video Technology, 2024
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
support 1representative citing papers
citing papers explorer
-
Paired-Sampling Contrastive Framework for Joint Physical-Digital Face Attack Detection
A paired-sampling contrastive framework unifies physical and digital face attack detection, achieving a 2.10% ACER on the 6th Face Anti-Spoofing Challenge.