AIFIND stabilizes incremental face forgery detection by aligning volatile features to invariant semantic anchors from low-level artifacts using attention and harmonization modules.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Face-D²CL fuses spatial and frequency features and uses dual continual learning to reduce forgetting while adapting to new DeepFakes, cutting average error rates by 60.7% and raising unseen-domain AUC by 7.9% over prior SOTA.
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AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection
AIFIND stabilizes incremental face forgery detection by aligning volatile features to invariant semantic anchors from low-level artifacts using attention and harmonization modules.
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Face-D(^2)CL: Multi-Domain Synergistic Representation with Dual Continual Learning for Facial DeepFake Detection
Face-D²CL fuses spatial and frequency features and uses dual continual learning to reduce forgetting while adapting to new DeepFakes, cutting average error rates by 60.7% and raising unseen-domain AUC by 7.9% over prior SOTA.