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: 2018 IEEE International Workshop on Information Forensics and Security (WIFS)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Video forgeries are detectable via binary classification on multimedia stream descriptors without pixel analysis.
citing papers explorer
-
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.
-
We Need No Pixels: Video Manipulation Detection Using Stream Descriptors
Video forgeries are detectable via binary classification on multimedia stream descriptors without pixel analysis.