# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 3 Fake Audio Detection Methods
[33] developed two novel models that depend on a convolution RNN for audio Deepfake classification. First, the Convolution Recurrent Neural Network Spoof (CRNN-Spoof) model contains five layers of extracted audio signals that are fed into a bidirectional LSTM network for predicting fake audio. Second, the Wide Inception Residual Network Spoof (WIRE-Net-Spoof) model has a different training process and uses a function named weighted negative log-likelihood. The CRNN-Spoof method obtained higher results than the WIRE-Net-Spoof approach by 0.132% of the Tandem Decision Cost Function (t-DCF) (t-DCF) is a single scalar that measure the reliability of decisions made by the systems [34]) with a 4.27% EER in the ASV spoof challenge 2019 dataset [19]. One limitation of this study is that it used many layers and convolutional networks, which caused it to suffer from management complexities. To address this limitation, [PERSON] and [PERSON] [35] proposed an alignment technique based on the classification models: Long Short-Term Memory (LSTM), bidirectional LSTM, and transformer architectures. The technique classifies each audio frame as matching or nonmatching from 50 recordings. The results reported that bidirectional LSTM outperforms the other models with a 99.7% accuracy and 0.43% EER. However, the training process took a long time, and the dataset used in the study was small, which led to overfitting. In regard to transfer learning and unimodal methods, [PERSON] et al. [36] proposed a new framework based on transfer learning and the ResNet-34 method for detecting faked English-speaking voices. The transfer learning model was pretrained on the CNN network. The Rest-34 method was used for solving the vanishing gradient problem that always occurs in any DL model. The results showed that the proposed framework achieved the best results measured by the EER and t-DCF metrics with results of 5.32% and 0.1514%, respectively. Although ResNet-34 solves the vanishing gradient issue, training takes a long time because of its deep architecture. Similarly, [PERSON] et al. [37] investigated feature-based and image-based approaches for classifying faked audio generated synthetically. New DL models called the Temporal Convolutional Network (TCN) and Spatial Transformer Network (STN) were used in this work. TCN achieved promising outcomes in distinguishing between fake and real audio with 92% accuracy, while STN obtained an accuracy of 80%. Although the TCN works well with sequential data, it does not work with inputs converted to Short-Time Fourier Transform (STFT) and Mel Frequency Cepstral Coefficients (MFCC) features. [PERSON] et al. [38] contributed a new Deepfake dataset named FakeAVCeleb [39]. The authors investigated unimodal methods that contain five classifiers to evaluate their efficiency in detection; the classifiers were MesoInception-4, Meso-4, Xception, EfficientNet-B0, and VGG16. The Xception classifier was found to achieve the highest performance with a result of 76%, while EfficientNet-B0 had the worst performance with a result of 50%. They concluded that none of the unimodal classifiers were effective for detecting fake audio. [PERSON] et al. [40] highlighted the need to develop a system for AD detection based on residual CNN. The main idea of this system is to extract three crucial features from the input, MFCC, constant Q cepstral coefficients (CQCC), and STFT, to determine the Counter Major (CM) score of the faked audio. A high CM score proves that the audio is real speech, while a low CM score suggests that it is fake. The proposed system showed promising results, improving the CM rate by 71% and 75% in two matrices of t-DCF (0.1569) and EER (6.02), respectively. However, further investigation is still needed due to the generalization errors in the proposed system. [PERSON] et al. [41] developed a new audio feature descriptor called ELTP-LFCC based on a Local Ternary Pattern (ELTP) and Linear Frequency Cepstral Coefficients (LFCC).