Generalizable Audio Spoofing Detection using Non-Semantic Representations
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.
Cite
@article{arxiv.2509.00186,
title = {Generalizable Audio Spoofing Detection using Non-Semantic Representations},
author = {Arnab Das and Yassine El Kheir and Carlos Franzreb and Tim Herzig and Tim Polzehl and Sebastian Möller},
journal= {arXiv preprint arXiv:2509.00186},
year = {2025}
}