# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 3 Fake Audio Detection Methods  
The wide range of accessible tools and methods capable of generating fake audio has led to significant recent attention to AD detection with different languages. This section will therefore present the latest work on detecting imitated and synthetically produced voices. In general, the current methods can be divided into two main types: ML and DL methods. Classical ML models have been widely adopted in AD detection. [PERSON] et al. [3] contributed to the literature on detecting fake audio in two aspects. They first developed a fake audio dataset based on the imitation method by extracting the entropy features of real and fake audio. Using the created H-Voice dataset [16], the researchers were able to build an ML model using Logistic Regression (LR) to detect fake audio. The model achieved a 98% success rate in detecting tasks, but the data needed to be pre-processed manually to extract the relevant features. [PERSON] and [PERSON] [17] proposed a Quadratic Support Vector Machine (Q-SVM) model to distinguish synthetic audio from natural human voices. When adopting the model for binary classification, the authors divided the audio into two classes, human and AI-generated. This model was compared to other ML methods, such as Linear Discriminant, Quadratic Discriminant, Linear SVM, weighted K-Nearest Neighbors (KNN), boosted tree ensemble, and LR. As a result, they found that Q-SVM outperformed other classical methods by 97.56%, with a misclassification rate of 2.43%. Moreover, [PERSON] et al. [18] developed an SVM model with Random Forest (RF) to predict synthetic voices based on a new audio feature called Short-Term Long-Term (STLT). The models were trained using the Automatic Speaker Verification (ASV) spoof challenge 2019 [19] dataset. Experiments found that the performance of SVM was higher than that of RF by 71%. [PERSON] et al. [20] compared  
Figure 3: The Synthetic-based Deepfake Process. the robustness of SVM with the DL method called Convolutional Neural Network (CNN) to detect the faked stereo audio from the real ones. From that comparison, it was found that CNN is more robust than SVM even though both achieved a high accuracy of 99% in the detection. However, SVM suffered from what the LR model had faced in the feature extraction process. According to the works discussed thus far, the features in the ML models need to be manually extracted, and intensive preprocessing is needed before training to ensure good performance. However, this is time-consuming and can lead to inconsistencies, which has led the research community to develop high-level DL methods.. To address this, the CNN model used by [PERSON] and [PERSON] [21] created a novel approach for detecting synthetic audio based on two CNN models, EfficientCNN and RES-EfficientCNN. As a result, RES-EfficientCNN achieved a higher F1-score of 97.61 than EfficientCNN (94.14 F1-score) when tested over the ASV spoof challenge 2019 dataset [19]. [PERSON] et al. [5] developed a classification model named Deep4 SNet that visualized the audio dataset based on a 2D CNN model (histogram) to classify imitation and synthetic audio. Deep4 SNet showed an accuracy of 98.5% in detecting imitation and synthetic audio. However, Deep4 SNet's performance was not scalable and was affected by the data transformation process. [PERSON] and [PERSON] [22] compared the performance of the CNN model against the random method in detecting synthetic audio signals. Although the CNN achieved an accuracy 85.99% higher than that of the baseline classifier, it suffered from an overfitting problem. The [PERSON] et al. [23] experimental study compared CNN and Bidirectional Long Short-Term Memory (BiLSTM) performance with ML models. The proposed method targeted the imitation-based fakeness of the Quranic audio clips dataset named Arabic Diversified Audio (AR-DAD) [24]. They tested the ability of CNN and BiLSTM to distinguish real voices from imitators.