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

2026-09-17

AI Summary

为了解决音频深度伪造检测中泛化到未知攻击的问题，提出CoRELoop方法，通过轻量级模块和低秩适配器在不改变原模型参数的情况下提升检测性能。

Abstract

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 lightweight refinement modules and loop-specific low-rank adapters on the original data, CoReLoop enables additional refinement while preserving the detector's original first-pass prediction. On 14 cross-domain test sets, the 24-layer model reduces pooled equal error rate (EER) from 4.85% to 3.74% with two passes, with approximately 10M trainable parameters out of 598M. To selectively apply this refinement, an optional halting head chooses the depth for each utterance, achieving 3.73% pooled EER with an average of 1.18 passes.

Problem

• audio deepfake detection
• generalization
• unseen attacks
• training data

Innovation

• recurrent refinement
• parameter-efficient
• controlled state updates
• low-rank adapters
• halting head