most citedSource Tracing of Audio Deepfake Systems

22 citations · 30 across the 5 of their papers we have counts for

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5 papers

eess.AS20245 cited

ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale

Xin Wang, Hector Delgado, Hemlata Tak +10

ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previou…

eess.AS202422 cited

Source Tracing of Audio Deepfake Systems

Nicholas Klein, Tianxiang Chen, Hemlata Tak +2

Recent progress in generative AI technology has made audio deepfakes remarkably more realistic. While current research on anti-spoofing systems primarily focuses on assessing wheth…

cs.SD2024

Harder or Different? Understanding Generalization of Audio Deepfake Detection

Nicolas M. Müller, Nicholas Evans, Hemlata Tak +2

Recent research has highlighted a key issue in speech deepfake detection: models trained on one set of deepfakes perform poorly on others. The question arises: is this due to the c…

cs.CR20233 cited

t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators

Tomi Kinnunen, Kong Aik Lee, Hemlata Tak +2

Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-sy…

eess.AS2023

Towards single integrated spoofing-aware speaker verification embeddings

Sung Hwan Mun, Hye-jin Shim, Hemlata Tak +12

This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well a…