An RFP dataset for Real, Fake, and Partially fake audio detection

Alali, Abdulazeez and Theodorakopoulos, George ORCID: https://orcid.org/0000-0003-2701-7809 2024. An RFP dataset for Real, Fake, and Partially fake audio detection. Presented at: Springer 9th International Conference on Cyber Security, Privacy in Communication Networks (ICCS2023), Cardiff, Wales, UK, 11-12 December 2023. AI Applications in Cyber Security and Communication Networks. Lecture Notes in Networks and Systems (1032) Springer, 10.1007/978-981-97-3973-8_1

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Abstract

Recent advances in deep learning have enabled the creation of natural-sounding synthesised speech. However, attackers have also utilised these tech-nologies to conduct attacks such as phishing. Numerous public datasets have been created to facilitate the development of effective detection models. How-ever, available datasets contain only entirely fake audio; therefore, detection models may miss attacks that replace a short section of the real audio with fake audio. In recognition of this problem, the current paper presents the RFP da-taset, which comprises five distinct audio types: partial fake (PF), audio with noise, voice conversion (VC), text-to-speech (TTS), and real. The data are then used to evaluate several detection models, revealing that the available detec-tion models incur a markedly higher equal error rate (EER) when detecting PF audio instead of entirely fake audio. The lowest EER recorded was 25.42%. Therefore, we believe that creators of detection models must seriously consid-er using datasets like RFP that include PF and other types of fake audio.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer
ISBN:	978-981-97-3972-1
Date of First Compliant Deposit:	24 April 2024
Last Modified:	05 Dec 2024 12:15
URI:	https://orca.cardiff.ac.uk/id/eprint/167934

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