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Self-Supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use adversarially synthesized forgeries and configuration prediction | Xception deepfake detector · CelebDF | AUC: 0.734 -> 0.797 | Helped |
| Add adversarial augmentation without self-supervised tasks (ablation) | Xception · FF++ cross-method generalization | AUC (Avg.): 0.669 -> 0.709 | Helped |
| Replace adversarial augmentation with random configuration selection (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.769 -> 0.716 | Hurt |
| Use adversarial rather than random configuration selection (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.716 -> 0.769 | Helped |
| Replace forgery-specific synthesis with random general image operations (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.769 -> 0.710 | Hurt |
| Replace forgery-specific synthesis with synthesizer-selected general image operations (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.769 -> 0.711 | Hurt |
| Replace the proposed augmentation with fixed-region forgery augmentation (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.769 -> 0.717 | Hurt |
| Replace the proposed augmentation with another fixed-region forgery augmentation (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.769 -> 0.722 | Hurt |
| Add blending-ratio estimation as an auxiliary task (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.733 -> 0.769 | Helped |
| Add blending-type estimation as an auxiliary task (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.737 -> 0.769 | Helped |
| Add forgery-region estimation as an auxiliary task (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.724 -> 0.769 | Helped |
| Add all three self-supervised auxiliary tasks (ablation) | SLADD · FF++ cross-method generalization | AUC (Avg.): 0.709 -> 0.769 | Helped |
Closest papers by meaning
- Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection (CVPR 2024 · similarity 0.88)
- From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes (NeurIPS 2025 · similarity 0.88)
- DF40: Toward Next-Generation Deepfake Detection (NeurIPS 2024 · similarity 0.87)
- Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection (AAAI 2024 · similarity 0.87)
- DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion (NeurIPS 2024 · similarity 0.87)
- Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector (CVPR 2025 · similarity 0.87)
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