DAWF introduces isolated identity attribution spaces and selective regional supervision to unify detection, localization, and source tracing for multi-face deepfakes.
Faceguard: Proactive deepfake detection
2 Pith papers cite this work. Polarity classification is still indexing.
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GenD achieves state-of-the-art average cross-dataset AUROC in deepfake detection by parameter-efficient adaptation of a foundational vision encoder with hyperspherical manifold enforcement via L2 normalization and metric learning.
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Whether, Which, and Whose: Solving the Triple Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios
DAWF introduces isolated identity attribution spaces and selective regional supervision to unify detection, localization, and source tracing for multi-face deepfakes.
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Deepfake Detection that Generalizes Across Benchmarks
GenD achieves state-of-the-art average cross-dataset AUROC in deepfake detection by parameter-efficient adaptation of a foundational vision encoder with hyperspherical manifold enforcement via L2 normalization and metric learning.