A chronological continual learning study finds deepfake detectors retain past knowledge but generalize to future generators at near-random AUC around 0.5.
Faster than lies: Real-time deepfake detection using binary neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization
A chronological continual learning study finds deepfake detectors retain past knowledge but generalize to future generators at near-random AUC around 0.5.