  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.
