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Face Forensics in the Wild

CVPR 2021 · Video › Video segmentation and tracking · approach: Deepfake video detection · 139 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the discriminative attention model instead of I3DFace forgery detection · FFIW-10K testAUC (%): 69.5 -> 70.9Helped
Train the proposed model on FFIW-10K rather than FF++Face forgery classification · DFDC Preview testAUC (%): 72.8 -> 74.1Helped
Train the proposed model on FFIW-10K rather than FF++Face forgery classification · Celeb-DF testAUC (%): 75.3 -> 78.3Helped
Train the proposed model on FFIW-10K rather than FF++Face forgery classification · FF++ testAUC (%): 99.5 -> 99.3Hurt
Replace multi-temporal-scale instance aggregation with max-pooling (ablation)Ours · FFIW-10K testAUC (%): 70.9 -> 66.2Hurt
Replace multi-temporal-scale instance aggregation with avg-pooling (ablation)Ours · FFIW-10K testAUC (%): 70.9 -> 65.7Hurt
Add short-term aggregation (ablation)Ours · FFIW-10K testAUC (%): 69.7 -> 70.9Helped
Add long-term aggregation (ablation)Ours · FFIW-10K testAUC (%): 70.4 -> 70.9Helped
Replace attention-based bag aggregation with max-pooling (ablation)Ours · FFIW-10K testAUC (%): 70.9 -> 69.5Hurt
Replace attention-based bag aggregation with avg-pooling (ablation)Ours · FFIW-10K testAUC (%): 70.9 -> 66.8Hurt
Add sparsity regularization loss (ablation)Ours · FFIW-10K testmAP (%): 28.5 -> 30.8Helped
Add sparsity regularization loss (ablation)Ours · FFIW-10K testAUC (%): 70.3 -> 70.9Helped

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