A dual-encoder deepfake detector pairs a frozen specialist with a LoRA-tuned MLLM, trained first via binary alignment then via RL to reward explain-then-classify behavior, yielding improved cross-dataset performance and interpretability.
Generalizing deepfake video detection with plug-and-play: Video-level blending and spatiotemporal adapter tuning
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.CV 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
ReAlign distills LLM-generated reasoning texts into a lightweight AIGI forgery detector via contrastive image-text alignment to improve generalization on complex forgeries.
Cross-AUC averages per-domain AUCs with a polarization term from Wasserstein distance on score distributions to assess deepfake detector generalization under domain shift more realistically than isolated AUC.
3D CNN detector with temporal consistency regularizer reaches 92.8% accuracy on DeepfakeTIMIT and 76.4% cross-dataset on FaceForensics++ without fine-tuning.
citing papers explorer
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The Regularizing Power of Language-Training Deepfake Detectors
A dual-encoder deepfake detector pairs a frozen specialist with a LoRA-tuned MLLM, trained first via binary alignment then via RL to reward explain-then-classify behavior, yielding improved cross-dataset performance and interpretability.
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ReAlign: Generalizable Image Forgery Detection via Reasoning-Aligned Representation
ReAlign distills LLM-generated reasoning texts into a lightweight AIGI forgery detector via contrastive image-text alignment to improve generalization on complex forgeries.
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When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
Cross-AUC averages per-domain AUCs with a polarization term from Wasserstein distance on score distributions to assess deepfake detector generalization under domain shift more realistically than isolated AUC.
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Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks
3D CNN detector with temporal consistency regularizer reaches 92.8% accuracy on DeepfakeTIMIT and 76.4% cross-dataset on FaceForensics++ without fine-tuning.