FMSD improves cross-dataset generalization in deepfake detection by using gradient-based layer masking to select forgery-sensitive weights and SVD to split them into preserved semantic and multiple learnable artifact subspaces with orthogonality constraints.
Partial is better than all: Revisiting fine-tuning strategy for few-shot learning,
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Generalizable Deepfake Detection Based on Forgery-aware Layer Masking and Multi-artifact Subspace Decomposition
FMSD improves cross-dataset generalization in deepfake detection by using gradient-based layer masking to select forgery-sensitive weights and SVD to split them into preserved semantic and multiple learnable artifact subspaces with orthogonality constraints.