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Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection

CVPR 2023 · Detection and segmentation › CNN features · approach: Forgery detection · 150 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add the content-guided CAFÉ frequency extraction module (ablation)SFDG baseline · FF++ (LQ)Acc: 88.69 -> 89.52Helped
add the content-guided CAFÉ frequency extraction module (ablation)SFDG baseline · FF++ (LQ)AUC: 90.4 -> 93.37Helped
add MDAML to CAFÉ (ablation)SFDG · FF++ (LQ)Acc: 89.52 -> 91.48Helped
add MDAML to CAFÉ (ablation)SFDG · FF++ (LQ)AUC: 93.37 -> 94.79Helped
add DG-SF3Net to CAFÉ (ablation)SFDG · FF++ (LQ)Acc: 91.48 -> 92.28Helped
add DG-SF3Net to CAFÉ (ablation)SFDG · FF++ (LQ)AUC: 94.79 -> 95.98Helped
increase graph neighbor count from k=10 to k=15 (ablation)SFDG · WildDeepfakeAcc: 84.41 -> 83.57Hurt
increase graph neighbor count from k=10 to k=15 (ablation)SFDG · WildDeepfakeAUC: 92.57 -> 91.15Hurt
increase graph neighbor count from k=10 to k=20 (ablation)SFDG · FF++ (LQ)AUC: 95.98 -> 91.59Hurt
use SFDG rather than PELdeepfake detection · FF++ (LQ)Acc: 90.52 -> 92.28Helped
use SFDG rather than PELdeepfake detection · WildDeepfakeAUC: 91.62 -> 92.57Helped

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