# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 3 Fake Audio Detection Methods
In addition, ML methods such as SVM, SVM-Linear, Radial Basis Function (SVMRBF), LR, Decision Tree (DT), RF, and Gradient Boosting (XGBoost) were also tested. Ultimately, the study found that SVM had the highest accuracy with 99%, while the lowest was DT with 73.33%. Meanwhile, CNN achieved a detection rate higher than BiLSTM with 94.33%. Although the accuracy of the CNN method was lower than that of the ML models, it was better in capturing spurious correlations. It was also effective in extracting features that could be achieved automatically with generalization abilities. However, the main limitation of the CNN models that are used thus far for AD is that they can only handle images as input, and thus the audio needs to be preprocessed and transformed to a spectrogram or 2D figure to be able to provide it as input to the network. [PERSON] et al. [25] proposed a 1-D CNN and Siamese CNN to detect fake audio. In the case of the 1-D CNN, the input to the model was the speech log-probabilities, while the Siamese CNN was based on two trained GMM models. The Siamese CNN contained two identical CNNs that were the same as the 1-D CNN but concatenated them using a fully connected layer with a softmax output layer. The two models were tested over the ASVspoof 2019 dataset to find that the proposed Siamese CNN outperformed the GMM and 1-D CNN by improving the min-tDCF and Equal Error Rate (EER) (EER is the error rate where the false-negative rate and the false-positive rate are equal [26]) by \(\sim\)55% when using the LFCC features. However, the performance was slightly lower when using the CQCC features. It was also found that the model is not sufficiently robust and works with a specific type of feature. Another CNN model was proposed in [27], where the audio was transferred to scatter plot images of neighboring samples before giving it as input to the CNN model. The developed model was trained over a dataset called the Fake or Real (FoR) dataset [28] to evaluate the model, and the model accuracy reached 88.9%. Although the proposed model addressed the generalization problem of DL-based models by training with data from different generation algorithms, its performance was not as good as the others in the literature. The accuracy (88%) and EER (11%) were worse than those of the other DL models tested in the experiment. Hence, the model needs further improvement, and more data transformers need to be included. On the other hand, [PERSON] et al. [29] proposed a new scoring method named Human Log-Likelihoods (HLLs) based on the Deep Neural Network (DNN) classifier to enhance the detection rate. They compared this with a classical scoring method called the Log-Likelihood Ratios (LLRs) that depends on the Gaussian Mixture Model (GMM). DNN-HLLs and GMM-LLRs have been tested with the ASV spoof challenge 2015 dataset [30] and extracted features automatically. These tests confirmed that DNN-HLLs produced better detection results than GMM-LLRs since they achieved an EER of 12.24. [PERSON] et al. [31] therefore developed a DNN model named Deep-Sonar that captured the neuron behaviors of speaker recognition (SR) systems against AI-synthesized fake audio. Their model depends on Layer-wise neuron behaviors in the classification task. The proposed model achieved a detection rate of 98.1% with an EER of approximately 2% on the voices of English speakers from the FoR dataset [28]. However, DeepSonar's performance was highly affected by real-world noise. [PERSON] et al. 's [32] research used DNNs to differentiate synthetic and real voices and combined two DL models, CNNs and Recurrent Neural Network (RNN). This is because CNN is efficient at extracting features, while RNN is effective at detecting long-term dependencies in time variances. Interestingly, this combination achieved a 94% success rate in detecting audio generated by AI synthesizers. Nevertheless, the DNN model does not carry much artifact information from the feature representation perspective. [PERSON] et al.