  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.
