# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 8 Assessing State-of-the-Art Defensive Measures
### PartialSpoof: Detection of Short Fake Speech Segments  
The PartialSpoof Database and Countermeasures paper [73] introduces the PartialSpoof database, which consists of short fake speech segments inserted into authentic utterances. To create these synthetic speech segments, the authors spliced synthetic speech into real recordings. The team developed countermeasures that leverage various feature extraction techniques and machine learning algorithms to differentiate between the real and fake speech segments. The proposed approach aims to detect short, fake speech segments that may be present in real recordings.  
The researchers conducted a study that considered the possibility of an attacker embedding a fake segment using various audio segment lengths.  
**Procedure.** The research assumes that fixed audio lengths should be generated for each segment within the audio file to test a dataset using the proposed CM. However, this assumption is an uncommon construction method for creating fake and partially fake audio since the detection tool should detect any fake segment regardless of the segment lengths. To begin with, we reproduced results similar to those listed in the paper by running the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detection Methods** & \multicolumn{2}{c}{**SSL\_Anti-spoofing**} & \multicolumn{2}{c}{**M2S-ADD**} & \multicolumn{2}{c}{**PartialSpoof**} \\ \hline Dataset & ASVspoof 2021 & RFP & ASVspoof 2019 & RFP & PS & RFP \\ EER & 0.82\% & 50.16\% & 1.34\% & 18.69\% & 0.49\% & 3.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: EER for three detection models: SSL_Anti-spoofing, M2S-ADD, and PartialSpoof.  
code using the provided trained model and dataset. After that, we created a new PF subset based on the real and entirely fake RFP subsets, resulting in a total of 50,650 PF speech files for this experiment. The location of fake segments was similar to the original PF subset in the RFP dataset, which was explained earlier in Section 5.2 (Table 1). The only difference was that each segment length was equal to 640 ms, which was the segment length that existed in Partial spoof experiments. Finally, we trained the model using our custom data, the 50,650 PF speech files.  
**Result.** After testing the proposed CM using training, validation, and evaluation sets of the RFP dataset, the EER result was 3.70%. While the proposed PartialSpoof CM model achieved a low EER 0.49% when trained on the proposed PS dataset, the detection efficiency was affected when using the RFP dataset. Additionally, in the PartialSpoof Database and Countermeasures paper [73], the authors demonstrated that when evaluating the ASVspoof 2019 LA dataset using a pre-trained model based on PartialSpoof dataset, the result was 0.90% EER. We carried out the same test using the RFP evaluation subset, and the result was as high as 64.63% EER. This suggests that the pre-trained model on the PartialSpoof dataset is not capable of dealing with unseen PF data. Furthermore, we believe that the low EER of 0.90% achieved by the proposed CM model on unseen ASVspoof 2019 LA data was due to the identical fake segment generation methods used in the PartialSpoof dataset, which the CM model was trained on.