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CoRELoop: Parameter-Efficient Controlled Recurrent Refinement for Audio Deepfake Detection

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Generalizing to unseen attacks remains challenging for audio deepfake detectors, and collecting training data covering all potential attacks is impractical. We explore recurrent refinement in an already-trained SSL-based detector without additional data or changes to its original parameters. However, directly recycling encoder outputs as inputs degrades detection in our diagnostic. We propose CoReLoop, which makes this reuse effective by adapting recurrent inputs to the frozen encoder, controlling state updates, and aligning refined outputs with the frozen classifier. By training only lightwei

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