DF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts
Audio-visual deepfake detection is an actively studied topic, where one of the main challenges is to develop detectors able to generalize across deepfake generation methods. We conjecture that overfitting can be mitigated by extracting multiple high-level cues from the available audio and visual modalities via pre-trained models. We therefore assemble a wide variety of pre-trained models to extract features that encode mouth movements, face parsing, facial expressions, head pose, gaze tracking, heart rate, audio emotion and speech activity. We further integrate both unimodal and multimodal cue
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- LinkedLinked via arxiv author · 85%Vlad Hondru →
“DF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts”
- LinkedLinked via arxiv author · 85%Florinel Alin Croitoru →
“DF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts”
- LinkedLinked via arxiv author · 85%Iuliana Georgescu →
“DF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts”
- LinkedLinked via arxiv author · 85%A. Sophia Koepke →
“DF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts”
- LinkedLinked via arxiv author · 85%Radu Tudor Ionescu →
“DF-MoE: Generalizable Deepfake Detection via Multimodal Sparse Mixture-of-Experts”