Cross-AUC averages per-domain AUCs with a polarization term from Wasserstein distance on score distributions to assess deepfake detector generalization under domain shift more realistically than isolated AUC.
FakeFormer: Efficient vulnerability-driven transformers for generalisable deepfake detection
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
3D CNN detector with temporal consistency regularizer reaches 92.8% accuracy on DeepfakeTIMIT and 76.4% cross-dataset on FaceForensics++ without fine-tuning.
citing papers explorer
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When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
Cross-AUC averages per-domain AUCs with a polarization term from Wasserstein distance on score distributions to assess deepfake detector generalization under domain shift more realistically than isolated AUC.
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Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks
3D CNN detector with temporal consistency regularizer reaches 92.8% accuracy on DeepfakeTIMIT and 76.4% cross-dataset on FaceForensics++ without fine-tuning.