Cross-AUC exposes large robustness drops in existing face forgery detectors across datasets, while the SFAM model with semantic alignment and region-specific experts delivers better performance on public benchmarks.
In ictu oculi: Exposing AI created fake videos by detecting eye blinking
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Video forgeries are detectable via binary classification on multimedia stream descriptors without pixel analysis.
Lightweight fusion of WDF with SPSL or LBP cues into Xception improves AUC by 3.8-4.4% on FaceForensics++ and DFDC-Preview with negligible parameter overhead compared to larger frequency-based detectors.
A new cross-dataset AUC metric exposes generalization gaps in deepfake detectors, and a CLIP-based framework with region-aware experts and patch-level alignment achieves state-of-the-art cross-domain robustness.
citing papers explorer
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Rethinking Cross-Domain Evaluation for Face Forgery Detection with Semantic Fine-grained Alignment and Mixture-of-Experts
Cross-AUC exposes large robustness drops in existing face forgery detectors across datasets, while the SFAM model with semantic alignment and region-specific experts delivers better performance on public benchmarks.
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We Need No Pixels: Video Manipulation Detection Using Stream Descriptors
Video forgeries are detectable via binary classification on multimedia stream descriptors without pixel analysis.
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Lightweight Complementary-Cue Fusion for Robust Video Face Forgery Detection
Lightweight fusion of WDF with SPSL or LBP cues into Xception improves AUC by 3.8-4.4% on FaceForensics++ and DFDC-Preview with negligible parameter overhead compared to larger frequency-based detectors.