Data-Centric Deepfake Detection: Enhancing Speech Generalizability
Published:Dec 20, 2025 04:28
•1 min read
•ArXiv
Analysis
This ArXiv paper proposes a data-centric approach to improve the generalizability of speech deepfake detection, a crucial area for combating misinformation. Focusing on data quality and augmentation, rather than solely model architecture, offers a promising avenue for robust and adaptable detection systems.
Key Takeaways
- •Highlights the importance of data quality and augmentation in deepfake detection.
- •Proposes a data-centric strategy, potentially leading to more robust detection systems.
- •Addresses the critical issue of generalizability in speech deepfake detection.
Reference
“The research focuses on a data-centric approach to improve deepfake detection.”