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Audio Deepfake Detection: A Survey

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arxiv 2308.14970 v1 pith:FRFOETAL submitted 2023-08-29 cs.SD eess.AS

Jiangyan Yi , Chenglong Wang , Jianhua Tao , Xiaohui Zhang , Chu Yuan Zhang , Yan Zhao This is my paper · ORCID
classification cs.SDeess.AS
keywords detectiondeepfakeaudiosurveydatasetsdevelopmentsevaluationfeatures
verification ladder T0 review T1 audit T2 compute T3 formal
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Audio deepfake detection is an emerging active topic. A growing number of literatures have aimed to study deepfake detection algorithms and achieved effective performance, the problem of which is far from being solved. Although there are some review literatures, there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences across various types of deepfake audio, then outline and analyse competitions, datasets, features, classifications, and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are discussed. In addition, we perform a unified comparison of representative features and classifiers on ASVspoof 2021, ADD 2023 and In-the-Wild datasets for audio deepfake detection, respectively. The survey shows that future research should address the lack of large scale datasets in the wild, poor generalization of existing detection methods to unknown fake attacks, as well as interpretability of detection results.

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Cited by 12 Pith papers

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  12. Speaker Privacy and Security in the Big Data Era: Protection and Defense against Deepfake

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