mlsanityData to 14 Oct 2026

Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes

arXiv 2505.23619 ↗First posted 29 May 2025cs.SD cs.LG eess.AScited by 1

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Abstract (arXiv)

Recent advancements in Text-to-Speech (TTS) models, particularly in voice cloning, have intensified the demand for adaptable and efficient deepfake detection methods. As TTS systems continue to evolve, detection models must be able to efficiently adapt to previously unseen generation models with minimal data. This paper introduces ADD-GP, a few-shot adaptive framework based on a Gaussian Process (GP) classifier for Audio Deepfake Detection (ADD). We show how the combination of a powerful deep embedding model with the Gaussian processes flexibility can achieve strong performance and adaptability. Additionally, we show this approach can also be used for personalized detection, with greater robustness to new TTS models and one-shot adaptability. To support our evaluation, a benchmark dataset is constructed for this task using new state-of-the-art voice cloning models.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 34 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

28 of 34 references.

References · 28

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16 more
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  1. 2026Non-Parametric Machine Text Detection via Multi-View Gaussian Processescited by 0