Genuine-Focused Learning using Mask AutoEncoder for Generalized Fake Audio Detection
Abstract
The generalization of Fake Audio Detection (FAD) is critical due to the emergence of new spoofing techniques. Traditional FAD methods often focus solely on distinguishing between genuine and known spoofed audio. We propose a Genuine-Focused Learning (GFL) framework guided, aiming for highly generalized FAD, called GFL-FAD. This method incorporates a Counterfactual Reasoning Enhanced Representation (CRER) based on audio reconstruction using the Mask AutoEncoder (MAE) architecture to accurately model genuine audio features. To reduce the influence of spoofed audio during training, we introduce a genuine audio reconstruction loss, maintaining the focus on learning genuine data features. In addition, content-related bottleneck (BN) features are extracted from the MAE to supplement the knowledge of the original audio. These BN features are adaptively fused with CRER to further improve robustness. Our method achieves state-of-the-art performance with an EER of 0.25% on ASVspoof2019 LA.
Cite
@article{arxiv.2406.03247,
title = {Genuine-Focused Learning using Mask AutoEncoder for Generalized Fake Audio Detection},
author = {Xiaopeng Wang and Ruibo Fu and Zhengqi Wen and Zhiyong Wang and Yuankun Xie and Yukun Liu and Jianhua Tao and Xuefei Liu and Yongwei Li and Xin Qi and Yi Lu and Shuchen Shi},
journal= {arXiv preprint arXiv:2406.03247},
year = {2024}
}
Comments
Accepted by INTERSPEECH 2024