EVID-Bench supplies 222 videos across nine manipulation types in three categories and shows that frontier multimodal models reach at most 61.43% point-level accuracy when forced to use web search to identify false information.
ArXivabs/2007.09355(2020)
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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.
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
Gradient preprocessing for image synthesis detection matches SOTA accuracy at low compute cost.
citing papers explorer
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When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection
EVID-Bench supplies 222 videos across nine manipulation types in three categories and shows that frontier multimodal models reach at most 61.43% point-level accuracy when forced to use web search to identify false information.
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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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Deepfake Detection Generalization with Diffusion Noise
ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
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Minimalist Preprocessing Approach for Image Synthesis Detection
Gradient preprocessing for image synthesis detection matches SOTA accuracy at low compute cost.