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arXiv · 2306.01428

Improved DeepFake Detection Using Whisper Features

Piotr Kawa·Marcin Plata·Michał Czuba·Piotr Szymański·Piotr Syga
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Abstract

With a recent influx of voice generation methods, the threat introduced by audio DeepFake (DF) is ever-increasing. Several different detection methods have been presented as a countermeasure. Many methods are based on so-called front-ends, which, by transforming the raw audio, emphasize features crucial for assessing the genuineness of the audio sample. Our contribution contains investigating the influence of the state-of-the-art Whisper automatic speech recognition model as a DF detection front-end. We compare various combinations of Whisper and well-established front-ends by training 3 detection models (LCNN, SpecRNet, and MesoNet) on a widely used ASVspoof 2021 DF dataset and later evaluating them on the DF In-The-Wild dataset. We show that using Whisper-based features improves the detection for each model and outperforms recent results on the In-The-Wild dataset by reducing Equal Error Rate by 21%.

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Piotr Kawa, Marcin Plata, Michał Czuba, Piotr Szymański, Piotr Syga. 2023-06-02. Improved DeepFake Detection Using Whisper Features. https://arxiv.org/abs/2306.01428

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