# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 3 Fake Audio Detection Methods
This descriptor was used with a Deep Bidirectional Long Short-Term Memory (DBiLSTM) network to increase the robustness of the model and to detect fake audio in diverse indoor and outdoor environmental conditions. The model created was tested over the ASVspoof 2019 dataset with synthetic and imitated-based fake audio. From the experiment, it was found that the model performed better over the audio synthetic dataset (with 0.74% EER) but not as well with imitated-based samples (with 33.28% EER). An anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT) method was proposed in [42] based on variants of the Squeeze-Excitation Network (SENet) and ResNet. This method uses log power magnitude spectra (logspec) and CQCC acoustic features to train the DNN. The model was tested with the ASVspoof 2019 dataset to find that ASSERT obtained more than a 17% relative improvements in synthetic audio. However, the model had zero t-DCF cost and zero EER with a logical access scenario during the test, which indicates that the model is highly overfitting. Based on the literature discussed thus far, we can say that although DL methods avoid manual feature extraction and excessive training, they still require special transformations for audio data. Consequently, self-supervised DL methods have recently been introduced into the AD detection literature. In particular, [PERSON] et al. [43] proposed a self-supervised spoofing audio detection (SSAD) model inspired by an existing self-supervised DL method named PASE+. The proposed model depends on multilayer convolutional blocks to extract context features from the audio stream. It was tested over the dataset with a 5.31% EER. While the SSAD did well in terms of efficiency and scalability, its performance was not as good as other DL methods. Future research could thus focus on the advantages of self-supervised learning and improving its performance. Ultimately, the literature discussed thus far is summarized in Table 1, which shows that the method type affects the performance more than the feature used. It is very clear that ML methods are more accurate than DL methods regardless of the features used. However, due to excessive training and manual feature extraction, the scalability of the ML methods is not confirmed, especially with large numbers of audio files. On the other hand, when DL algorithms were used, specific transformations were required on the audio files to ensure that the algorithms could manage them. In conclusion, although AD detection is an active area of study, further research is still needed to address the existing gaps. These challenges and potential future research directions will be highlighted in Section 6. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Ref. ** & **Speech Language** & **Fakeness Type** & **Technique** & **Audio Feature Used** & **Dataset** & **Drawbacks** \\ \hline
2018 & [PERSON] et al. [29] & English & Synthetic & DNN-HLL & MFCC, LFCC, CQCC & & & \ \begin{tabular}{c} ASV spoof \\ 2015 [30] \\ in the feature representations perspective. \\ \end{tabular}  & \begin{tabular}{c} The error rate is zero, indicating that the proposed DNN is overfitting. \\ \end{tabular}  \\ \cline{3-7}
2019 & [PERSON] et al. [40] & English & Synthetic & Residual CNN & MFCC, CQCC, STFT & \ \begin{tabular}{c} ASV spoof \\ 2019 [19] \\ \end{tabular}  & \begin{tabular}{c} The model is highly overfitting with \\ synthetic data and cannot be generalized \\ over unknown attacks. \\ \end{tabular}  \\ \hline
2019 & [PERSON] et al. [42] & English & Synthetic & ASSERT (SENet +  ResNet) & Logspec, CQCC & \ \begin{tabular}{c} ASV spoof \\ 2019 [19] \\ \end{tabular}  & \begin{tabular}{c} The model is highly overfitting with \\ synthetic data. \\ \end{tabular}  \\ \hline
2020 & [PERSON] et al. [54] & English & Synthetic & ResNet-34 & Spectrogram & \ \begin{tabular}{c} ASV spoof \\ 2019 [19] \\ \end{tabular}  &