Generalizable Audio Spoofing Detection using Non-Semantic Representations

• URL: http://arxiv.org/abs/2509.00186v1
• Date: Fri, 29 Aug 2025 18:37:57 GMT
• Title: Generalizable Audio Spoofing Detection using Non-Semantic Representations
• Authors: Arnab Das, Yassine El Kheir, Carlos Franzreb, Tim Herzig, Tim Polzehl, Sebastian Möller,
• Abstract summary: generative modeling has made synthetic audio generation easy, making speech-based services vulnerable to spoofing attacks.<n>Existing solutions for deepfake detection are often criticized for lacking generalizability and fail drastically when applied to real-world data.<n>This study proposes a novel method for generalizable spoofing detection leveraging non-semantic universal audio representations.
• Score: 12.685819931453045
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Rapid advancements in generative modeling have made synthetic audio generation easy, making speech-based services vulnerable to spoofing attacks. Consequently, there is a dire need for robust countermeasures more than ever. Existing solutions for deepfake detection are often criticized for lacking generalizability and fail drastically when applied to real-world data. This study proposes a novel method for generalizable spoofing detection leveraging non-semantic universal audio representations. Extensive experiments have been performed to find suitable non-semantic features using TRILL and TRILLsson models. The results indicate that the proposed method achieves comparable performance on the in-domain test set while significantly outperforming state-of-the-art approaches on out-of-domain test sets. Notably, it demonstrates superior generalization on public-domain data, surpassing methods based on hand-crafted features, semantic embeddings, and end-to-end architectures.

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