WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection

Abstract:Existing front-ends for speech deepfake detection are primarily categorized into two types. Hand-crafted filterbank features are transparent but limited in capturing higher-level information. SSL features, in turn, lack interpretability and may overlook fine-grained spectral anomalies. We propose WaveScat, a novel family of feature extractors that combines the best of both worlds via the wavelet scattering transform (WST), which cascades wavelet convolutions with modulus nonlinearities to produce deformation-stable, multi-scale features. Experiments on the recent Deepfake-Eval-2024 benchmark, together with cross-dataset evaluations on SpoofCeleb, In-the-Wild, and ASVspoof 5, show that WaveScat outperforms existing front-ends by a wide margin. Our analysis reveals that a small averaging scale combined with high-frequency and directional resolutions is critical for capturing subtle artifacts. This underscores the value of stable and translation-invariant features for speech deepfake detection. The code and supplementary materials are available at this https URL.

Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Signal Processing (eess.SP)

arXiv:2602.02980 [eess.AS]

(or arXiv:2602.02980v3 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2602.02980 Focus to learn more arXiv-issued DOI via DataCite

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