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arXiv AI By Kunyu Feng, Yuxiang Wang, Li Wang, Wan Lin, Zhizheng Wu

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

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CoReLoop introduces a parameter‑efficient refinement strategy for audio deepfake detection that reuses a frozen SSL‑based detector’s encoder outputs without altering its original parameters. By adapting recurrent inputs, controlling state updates, and aligning refined outputs with the frozen classifier, the method adds lightweight refinement modules and low‑rank adapters trained on the original data. On 14 cross‑domain test sets, the 24‑layer model reduces pooled equal error rate from 4.85% to 3.74% with two passes, and an optional halting head further improves performance to 3.73% with an average of 1.18 passes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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