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WaveFake: A Data Set to Facilitate Audio Deepfake Detection

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arxiv 2111.02813 v1 pith:546UKTSF submitted 2021-11-04 cs.LG cs.CRcs.SDeess.AS

Joel Frank , Lea Schönherr This is my paper
classification cs.LGcs.CRcs.SDeess.AS
keywords audioresearchdatafacilitateprocessingsignalsignalsadopted
verification ladder T0 review T1 audit T2 compute T3 formal
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Deep generative modeling has the potential to cause significant harm to society. Recognizing this threat, a magnitude of research into detecting so-called "Deepfakes" has emerged. This research most often focuses on the image domain, while studies exploring generated audio signals have, so-far, been neglected. In this paper we make three key contributions to narrow this gap. First, we provide researchers with an introduction to common signal processing techniques used for analyzing audio signals. Second, we present a novel data set, for which we collected nine sample sets from five different network architectures, spanning two languages. Finally, we supply practitioners with two baseline models, adopted from the signal processing community, to facilitate further research in this area.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Audio Deepfake Detection with Half-Truth Localisation Using Cross-Attentive Feature Fusion

    cs.SD 2026-05 unverdicted novelty 5.0 of 10

    CAFNet performs joint ternary classification and temporal boundary regression for half-truth audio deepfakes via cross-attentive fusion of MFCC, LFCC, and Chroma-STFT features, reporting 92.71% accuracy and 0.075s MAE...

  2. Audio Deepfake Detection at the First Greeting: "Hi!"

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    S-MGAA adds pixel-channel enhancement and frequency compensation modules to improve audio deepfake detection on very short, degraded speech inputs.

  3. Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios

    eess.AS 2025-04 unverdicted novelty 5.0 of 10

    The paper creates ADD-C benchmark dataset for audio deepfake detection under codec compression and packet loss, shows baseline degradation, and demonstrates a data augmentation method that boosts robustness.

  4. Teffic-Audio: Tell Fact from Fiction

    cs.SD 2026-07 conditional novelty 4.0 of 10

    A simple Conformer deepfake detector trained with multi-source balanced sampling and diverse augmentation reaches 1.454% pooled EER on Speech-DF-Arena, first among public systems.

  5. Detecting Audio Deepfakes on the Edge:Lightweight SSL-Based Detection in a Browser Plugin

    eess.AS 2026-06 unverdicted novelty 4.0 of 10

    Truncated SSL backbone with logistic classifier detects audio deepfakes on-device, claimed to outperform AASIST by 10% while running 40% faster, packaged as a browser plugin.

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