# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 5 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measures** & **Dataset** & **Detection Method** & **Results (The Result Is Approximate from the Evaluation Test Published in the Study)** \\ \hline \multirow{8}{*}{t-DCF} & \multirow{8}{*}{ASV spoof 2019 challenge} & Residual CNN [40] & 0.1569 \\ \cline{3-3}  & & SENet-34 [42] & 0.155 \\ \cline{3-3}  & & CRNN-Spoof [33] & 0.132 \\ \cline{3-3}  & & ResNet-34 [36] & 0.1514 \\ \cline{3-3}  & & Siamese CNN [25] & 0.211 \\ \cline{3-3}  & & CNN [25] & 0.217 \\ \cline{3-3}  & & DBiLSTM [41] (Synthetic Audio) & 0.008 \\ \cline{3-3}  & & DBiLSTM [41] (Imitation-based) & 0.39 \\ \hline \multirow{8}{*}{AR-DAD} & CNN [22] & 85.99\% \\ \cline{3-3}  & & SVM [18] & 71.00\% \\ \cline{3-3}  & & CNN [23] & 94.33\% \\ \cline{3-3}  & & BiLSTM [23] & 91.00\% \\ \cline{3-3}  & & SVM [23] & 99.00\% \\ \cline{3-3}  & & DT [23] & 73.33\% \\ \cline{3-3}  & & RF [23] & 93.67\% \\ \cline{3-3}  & & LR [23] & 98.00\% \\ \cline{3-3}  & & XGBoost [23] & 97.67\% \\ \cline{3-3}  & & SVMRBF [23] & 99.00\% \\ \cline{3-3}  & & SVM-LINEAR [23] & 99.00\% \\ \hline \multirow{8}{*}{Accuracy} & \multirow{8}{*}{FoR} & DNN [32] & 94.00\% \\ \cline{3-3}  & & Deep-Sonar [31] & 98.10\% \\ \cline{3-3}  & & STN [37] & 80.00\% \\ \cline{3-3}  & & TCN [37] & 92.00\% \\ \cline{3-3}  & & SVM [37] & 67\% \\ \cline{3-3}  & & RF [37] & 62\% \\ \cline{3-3}  & & KNN [37] & 62\% \\ \cline{3-3}  & & XGBoost [37] & 59\% \\ \cline{3-3}  & & LGBM [37] & 60\% \\ \cline{3-3}  & & CNN [27] & 88.00\% \\ \hline \multirow{8}{*}{FakeAVCeleb} & EfficientNet-B0 [38] & 50.00\% \\ \cline{3-3}  & & Xception [38] & 76.00\% \\ \cline{1-1} \cline{2-3}  & & MesoInception-4 [38] & 53.96\% \\ \cline{1-1} \cline{2-3}  & & Meso-4 [38] & 50.36\% \\ \cline{1-1} \cline{2-3}  & & VGG16 [38] & 67.14\% \\ \hline \multirow{2}{*}{H-Voice} & LR [3] & 98\% \\ \cline{3-3}  & & Deep4 SNet [5] & 98.5\% \\ \hline - & Q-SVM [17] & 97.56\% \\ \hline - & CNN [20] & 99\% \\ \hline - & SVM [20] & 99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._trained on the same dataset as DNN-HLLs. In regard to the CNN methods highlighted in the orange box, regardless of the dataset used, all versions have a similar performance with respect to the EER and t-DCF. However, one interesting observation that can be highlighted is the fact that the type of fakeness can have an effect on the performance of the method. For instance, DBiLSTM provides a very low EER and t-DCF compared to the other methods when applied with synthetic AD, while it is one of the worst when applied to the imitation-based datasets. When considering the accuracy, illustrated in Figure 6, a pattern can be identified with respect to the dataset and fakeness type. In particular, regardless of the method used, the accuracy of the methods applied on the FakeAVCeleb dataset did not perform well compared to the other datasets. This can be attributed to the fact that this dataset is a combination of video-audio Deepfakes. Moreover, ML methods perform better with imitation-based fakeness compared to the synthetic-based datasets. For instance, in SVM, RF, and XGBoost, highlighted with green, gray, and purple boxes, respectively, it is clear that they perform almost perfectly when applied to the imitation-based datasets (AR-DAD and H-voice) while preforming poorly when applied over the synthetic-based datasets (ASV spoof and FoR). Moreover, it is interesting to note that DL-based methods such as CNN are more stable than the ML methods with respect to the fakeness type. For instance, comparing CNN versions (under the orange box) with SVM versions (under the gray box), it is clear that CNN is stable and has a similar performance regardless of the dataset, while SVM is unstable and performs differently depending on the data and fakeness type. Thus, it can be concluded that regardless of how ML methods perform well, for more stability and consistency in performance, DL-based methods are better options.