# Unmasking real-world audio deepfakes: A data-centric approach

arXiv: https://arxiv.org/abs/2506.09606 · HTML: https://mlsanity.com/paper/2506.09606/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2025.

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

- [Attention-based Mixture of Experts for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2509.17585/) (2025)
- [QAMO: Quality-aware Multi-centroid One-class Learning For Speech Deepfake Detection](https://mlsanity.com/paper/2509.20679/) (2025)
- [Ethical and Technical Limits of Deepfake Speech Datasets](https://mlsanity.com/paper/2606.10911/) (2026)
- [On the Contribution of Lexical Features to Speech Emotion Recognition](https://mlsanity.com/paper/2509.05634/) (2025)
- [Dual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection](https://mlsanity.com/paper/2606.16532/) (2026)

## Abstract (arXiv)

The growing prevalence of real-world deepfakes presents a critical challenge for existing detection systems, which are often evaluated on datasets collected just for scientific purposes. To address this gap, we introduce a novel dataset of real-world audio deepfakes. Our analysis reveals that these real-world examples pose significant challenges, even for the most performant detection models. Rather than increasing model complexity or exhaustively search for a better alternative, in this work we focus on a data-centric paradigm, employing strategies like dataset curation, pruning, and augmentation to improve model robustness and generalization. Through these methods, we achieve a 55% relative reduction in EER on the In-the-Wild dataset, reaching an absolute EER of 1.7%, and a 63% reduction on our newly proposed real-world deepfakes dataset, AI4T. These results highlight the transformative potential of data-centric approaches in enhancing deepfake detection for real-world applications. Code and data available at: https://github.com/davidcombei/AI4T.