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DFD-Lab: A Modular Audio-Visual Deepfake Detection Pipeline
Comparing audio-visual deepfake detectors requires coordinating dataset adaptation, temporal input representation, model interfaces and experimental conditions. We present DFD-Lab, a modular pipeline that separates these responsibilities while supporting shared training and evaluation workflows. We integrate three implementations: Xception-based maximum-logit fusion, ResNet with temporal LSTM fusion, and our AVFF reimplementation. Experiments cover external testing, degradation-based training augmentation and evaluation-time corruption. On a filtered subset of Deepfake-Eval-2024, models traine
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- arXiv · AI, language, vision and robotics · 2026-09-20T19:29:40.000Z
First collected: 2026-09-23T09:51:33.063Z. This is not the publication date.