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Self-Supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection

CVPR 2022 · Label-efficient and robust learning › Self-supervised and contrastive · approach: Self-supervision in supervised pipelines · 293 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use adversarially synthesized forgeries and configuration predictionXception deepfake detector · CelebDFAUC: 0.734 -> 0.797Helped
Add adversarial augmentation without self-supervised tasks (ablation)Xception · FF++ cross-method generalizationAUC (Avg.): 0.669 -> 0.709Helped
Replace adversarial augmentation with random configuration selection (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.769 -> 0.716Hurt
Use adversarial rather than random configuration selection (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.716 -> 0.769Helped
Replace forgery-specific synthesis with random general image operations (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.769 -> 0.710Hurt
Replace forgery-specific synthesis with synthesizer-selected general image operations (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.769 -> 0.711Hurt
Replace the proposed augmentation with fixed-region forgery augmentation (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.769 -> 0.717Hurt
Replace the proposed augmentation with another fixed-region forgery augmentation (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.769 -> 0.722Hurt
Add blending-ratio estimation as an auxiliary task (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.733 -> 0.769Helped
Add blending-type estimation as an auxiliary task (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.737 -> 0.769Helped
Add forgery-region estimation as an auxiliary task (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.724 -> 0.769Helped
Add all three self-supervised auxiliary tasks (ablation)SLADD · FF++ cross-method generalizationAUC (Avg.): 0.709 -> 0.769Helped

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