# Authenticity at Risk: Key Factors in the Generation and Detection of Audio Deepfakes +
## 6 Results and Discussion
### The Influence of the Acoustic Context  
To analyze the factors that may influence detection, several aspects were evaluated. First, as detailed in Section 4, the acoustic context of each voice note was examined. The results regarding the percentage of audios misclassified based on their location are presented in Table 3. These results reveal a significant variability in classifier performance as a function of audio location. In particular, it is observed that in the locations _Bar_, _School_, _Cafeteria_, _Medical Center_ and _Restaurant_, the percentage of misclassified audios is 0%, indicating perfect performance in these contexts for all classifiers. The Linear SVM classifier stands out for its flawless performance in the _Work_ category, although it shows poor performance in _Transport_, with the highest percentage of errors. In contrast, the Naive Bayes classifier, despite its lower accuracy in _Home_, shows higher efficiency in _Transport_ and _Work_, with the lowest percentage of errors in these categories. Analysis of the KNN results reveals low performance in _Transport_, where half of the classification errors are concentrated, while the other 50% is distributed between _Home_ and _Work_. A similar pattern is observed in the Random Forest classifier, where almost half of the errors correspond to the _Transport_ category. These results suggest that, with the exception of Naive Bayes, classifying audios in the _Transport_ category is more challenging for the other classifiers. Figure 7 shows, for the four classifiers, the percentage of misclassified audios in each location relative to the total number of audios in that context. It is observed that in the _Transport_ category, 27.7% of the total number of audios in that context were misclassified by the Linear SVM and Random Forest classifiers, while in Naive Bayes this percentage amounts to 33.3% and in KNN to 44.4%. It is important to note that this location analysis was carried out only for the real audio, since in the case of the synthetic audio, all locations were classified as _Home_.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Linear SVM** &
\begin{tabular}{c} **Random** \\ **Forest** \\ \end{tabular} & **KNN** & **Naive Bayes** \\ \hline Home & 28.57\% & 41.66\% & 31.25\% & 50\% \\ \hline Transport & 71.42\% & 41.66\% & 50\% & 27.27\% \\ \hline Work & 0\% & 16.66\% & 18.75\% & 27.27\% \\ \hline Bar & 0\% & 0\% & 0\% & 0\% \\ \hline School & 0\% & 0\% & 0\% & 0\% \\ \hline Cafeteria & 0\% & 0\% & 0\% & 0\% \\ \hline Medical Center & 0\% & 0\% & 0\% & 0\% \\ \hline Restaurant & 0\% & 0\% & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentage of misclassified audios based on locations.  
Figure 7: Percentage of audios misclassified for each classifier by location. The values displayed in red represent the percentage of audio samples incorrectly classified relative to the total number of samples for that location. For example, for Naive Bayes, in the “Home” location, if the classifier displays 8.7% in red, it means that 8.7% of the total audio samples in that category are misclassified.