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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
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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.