# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 7 Partial Fake Speech and Human Speech
### User Study A: Can Users Identify Partial Fake Speech (as Audio-Only)? Our experiment aimed to investigate whether human listeners can differentiate between real and partially fake audio for both familiar and unfamiliar speakers. To achieve this, we designed the following survey:  
**Participants. ** We enrolled 148 participants through the online crowd-sourcing platform Prolific ([[https://www.prolific.co/](https://www.prolific.co/)]([https://www.prolific.co/](https://www.prolific.co/))). All participants identified themselves as native English speakers and were currently residing in either the United Kingdom or the United States. Half of the participants identified as female, while the other half identified as male. All the participants were aged 18 years or older. The survey was designed to take an average of six minutes to complete, and participants were compensated with $1 for their time. **Procedure. ** The participants were given an online survey that consisted of 10 audio and six video clips. The total duration of all audio and video recordings was approximately three minutes. The audio files contained speech that was real, fake, and partially fake. Six audio files were from unknown speakers, and the other four used [PERSON]'s voice. The participants were required to listen to these audio files and watch the six videos before submitting their answers. We used the Tortoise TTS zero-shot model to create partial fake speech segments. To generate the cloning voice of each speaker, we only used a six-second real speech recording. Additionally, we manually created the partial fake speech by replacing a word or two within a real speech. This manual editing of the audio was performed to make the transition between real and fake segments smoother and to create a more challenging speech for participants to distinguish. The audio sources included in this survey were either custom recordings made for this survey or audio extracted from YouTube 8M videos. **Result. ** We aimed to provide answers to the questions listed below:  
**(1)**_How accurately can participants distinguish the partial fake speeches?_ As we can see in Figure 7 and from the detailed results presented in Table 9, it is extremely difficult for humans to identify partially fake speech from both familiar and unfamiliar voices. The results show that most participants could not identify PF speech correctly: more than 75% of the participants were fooled by partially fake speech. **(2)**_Does listening to multiple samples from familiar speakers increase the ability to distinguish partial fake speech?_ We included four audio clips of [PERSON]'s voice in this study. These clips fall into four categories: entirely real, entirely fake, and two examples of partial fake (PF) speech. Our survey results highlight the difficulty humans face in identifying PF audio. As no previous studies have been conducted on partial fake audio, we compared our results to a user study [26] that focused on entirely fake audio. The results of that study showed that participants identified entirely fake voices 50% of the time for unfamiliar speakers and 80% of the time for familiar speakers. In our study, participants identified entirely fake voices for unfamiliar speakers 29.5% of the time, and for familiar speakers 69.5% of the time. Our study reveals that partial fake speech is a more \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Embedded Audi**} & \multirow{2}{*}{**Face Distance of Camera**} & \multicolumn{3}{c}{**Participants Responses Accuracy**} \\ \cline{3-5}  & & **Real** & **Fake** & **Partial Fake** \\ \hline PF & far & 85\% & 4\% & 11\% \\ PF & far & 63\% & 14\% & 23\% \\ PF & front & 77\% & 6\% & 17\% \\ PF & front & 52\% & 17\% & 31\% \\ Real & front & 69\% & 10\% & 21\% \\ Fake & Front/far & 60\% & 13\% & 27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Participants’ average responses to six video questions.