A separable prompt learning strategy on CLIP's text encoder enables competitive or superior generalizable performance in cross-dataset and cross-method face forgery detection.
Detecting deepfakes with self-blended images
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LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.
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Generalizable Face Forgery Detection via Separable Prompt Learning
A separable prompt learning strategy on CLIP's text encoder enables competitive or superior generalizable performance in cross-dataset and cross-method face forgery detection.
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LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection
LAA-X uses multi-task learning with explicit localized artifact attention and blending synthesis to build a deepfake detector that generalizes to high-quality and unseen manipulations after training only on real and pseudo-fake samples.