# Genuine-Focused Learning using Mask AutoEncoder for Generalized Fake Audio Detection

arXiv: https://arxiv.org/abs/2406.03247 · HTML: https://mlsanity.com/paper/2406.03247/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2024.

## Built on by (most cited first, 5 of 5)

- [Mixture of Experts Fusion for Fake Audio Detection Using Frozen wav2vec 2.0](https://mlsanity.com/paper/2409.11909/) (2024)
- [Does Current Deepfake Audio Detection Model Effectively Detect ALM-based Deepfake Audio?](https://mlsanity.com/paper/2408.10853/) (2024)
- [Temporal Variability and Multi-Viewed Self-Supervised Representations to Tackle the ASVspoof5 Deepfake Challenge](https://mlsanity.com/paper/2408.06922/) (2024)
- [Fake Speech Wild: Detecting Deepfake Speech on Social Media Platform](https://mlsanity.com/paper/2508.10559/) (2025)
- [Towards Generalized Source Tracing for Codec-Based Deepfake Speech](https://mlsanity.com/paper/2506.07294/) (2025)

## Abstract (arXiv)

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.