An RFP dataset for Real, Fake, and Partially fake audio detection
<|reference_start|>An RFP dataset for Real, Fake, and Partially fake audio detection: 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.<|reference_end|>
@article{alali2024an,
    title={An RFP dataset for Real, Fake, and Partially fake audio detection},
    author={Abdulazeez AlAli, George Theodorakopoulos},
    journal={arXiv preprint arXiv:2404.17721},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17721},
    primaryClass={cs.SD cs.CR eess.AS}
}