Data-Centric Deepfake Detection: Enhancing Speech Generalizability

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.

• •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.