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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

A number of AI-generated tools are used today to clone human voices, leading to a new technology known as Audio Deepfakes (ADs). Despite being introduced to enhance human lives as audiobooks, ADs have been used to disrupt public safety. ADs have thus recently come to the attention of researchers, with Machine Learning (ML) and Deep Learning (DL) methods being developed to detect them. In this article, a review of existing AD detection methods was conducted, along with a comparative description of the available faked audio datasets. The article introduces types of AD attacks and then outlines and analyzes the detection methods and datasets for imitation- and synthetic-based Deepfakes. To the best of the authors’ knowledge, this is the first review targeting imitated and synthetically generated audio detection methods. The similarities and differences of AD detection methods are summarized by providing a quantitative comparison that finds that the method type affects the performance more than the audio features themselves, in which a substantial tradeoff between the accuracy and scalability exists. Moreover, at the end of this article, the potential research directions and challenges of Deepfake detection methods are discussed to discover that, even though AD detection is an active area of research, further research is still needed to address the existing gaps. This article can be a starting point for researchers to understand the current state of the AD literature and investigate more robust detection models that can detect fakeness even if the target audio contains accented voices or real-world noises.

Details

Title
A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
Author
Almutairi, Zaynab 1   VIAFID ORCID Logo  ; Elgibreen, Hebah 2   VIAFID ORCID Logo 

 Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh P.O. Box 145111, Saudi Arabia; [email protected] 
 Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh P.O. Box 145111, Saudi Arabia; [email protected]; Artificial Intelligence Center of Advanced Studies (Thakaa), King Saud University, Riyadh P.O. Box 145111, Saudi Arabia 
First page
155
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
19994893
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2670042525
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.