Improved DeepFake Detection Using Whisper Features
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%.
Cite
@article{arxiv.2306.01428,
title = {Improved DeepFake Detection Using Whisper Features},
author = {Piotr Kawa and Marcin Plata and Michał Czuba and Piotr Szymański and Piotr Syga},
journal= {arXiv preprint arXiv:2306.01428},
year = {2023}
}
Comments
Accepted to INTERSPEECH 2023