ALDEN: Dual-Level Disentanglement with Meta-learning for Generalizable Audio Deepfake Detection

A significant challenge in audio deepfake detection (ADD) is to improve model generalization against unseen vocoders and other unknown factors, as existing methods often overfit to specific vocoder patterns or synthetic-irrelevant factors. To overcome this challenge, by focusing on vocoder-agnostic features and synthetic traces for generalizable ADD, we propose a novel dual-level disentanglement with meta-learning (ALDEN ) framework. Specifically, we first introduce an adversarial-training-based disentanglement learning (ADL) module to explicitly learn vocoder-agnostic and vocoder-specific features, effectively disentangling audio signals in terms of low-level characteristics. To suppress synthetic-irrelevant information, such as semantics and speaker identities, we simultaneously employ a reconstruction-based disentanglement learning (RDL) module, which further disentangles both synthetic-relevant and synthetic-irrelevant features from vocoder-agnostic features at a high-level of semantics. Additionally, as low-level non-semantic features are more critical in ADD, a vocoder-agnostic meta-learning (VAML) module is proposed to simulate cross-vocoder scenarios so as to further boost generalization performance. Extensive experiments demonstrate that ALDEN outperforms state-of-the-art methods in cross-vocoder and in-the-wild scenarios. The code, model, and supplementary materials will be released on the project page: https://beyond0814.github.io/ALDEN/.