# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Citation** & **Feature Extraction Technique** & **Description of the Feature Extraction Technique** \\ \hline [PERSON] and [PERSON] [65] & Xception CNN with LSTM fusion & The Xception CNN outputs 60 feature vectors, which are input into a two-layer LSTM network. \\ \hline [PERSON] et al. [66] & Multitask Cascaded Neural Network (MCNN) with EfficientNet-B5 and LSTM & An MCNN extracts face in pre-processing before an EfficientNet-B5 CNN extracts feature vectors. Subsequently, LSTM detects sequential inconsistencies. \\ \hline [PERSON] and [PERSON] [67] & EfficientNet CNN with LSTM & Frames are extracted using EfficientNet CNN, and the faces are saved instead of the full frame. The LSTM layer is used as input and is followed by two dense layers to classify the output. \\ \hline [PERSON] et al. [68] & Pre-trained CNN (ResNeXt-50) with three LSTM layers & Extracts features using ResNeXt-50 architecture with ReLU to activate the convolutional layers followed by three LSTM layers. \\ \hline [PERSON] [69] & VGG-16 and ResNet-50-based CNN with LSTM & A VGG16 and ResNet-50-based CNN model extracts features followed by an LSTM layer. \\ \hline [PERSON] et al. [70] & InceptionResNetV2 CNN with LSTM & InceptionResNetV2 CNN is used for feature extraction followed by LSTM for temporal analysis. \\ \hline [PERSON] et al. [71] & Pre-trained CNN (ResNeXt-50) with LSTM & Utilises ResNeXt-50 architecture with LSTM for feature extraction and temporal analysis, with a dropout rate of 0.4 applied to the LSTM layer for regularisation. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._methods like those by [PERSON] et al. (2018), which employ a triplet LSTM-RNN autoencoder with a VGGNet-16 backbone, show a shift towards combining autoencoders to capture both spatial and temporal dynamics simultaneously. Another point of divergence is the pre-processing step before feature extraction. [PERSON] et al. (2018) use Gaussian blur to extract facial noise, while [PERSON] et al. (2018) rely on frame consistency, showing that pre-processing methods differ greatly depending on the dataset and task focus. Moreover, temporal feature extraction techniques vary beyond LSTM usage. Some approaches incorporate more complex recurrent architectures, such as bidirectional LSTMs ([PERSON] et al. (2018)) or GRU layers ([PERSON] et al. (2018)), while others use simpler models. The choice between using unidirectional or bidirectional LSTMs and whether to augment the model with GRUs depends on the depth of temporal analysis required. Lastly, there are subtle variations in the regularisation and optimisation techniques applied. For example, [PERSON] et al. (2019) employ a ReLU activation function and dropout layer, while others, such as [PERSON] et al. (2019), adopt a dropout rate of 0.4 to prevent overfitting in their LSTM layers. Such differences, though subtle, can significantly impact model performance, depending on the complexity of the dataset. Table 5 summarises the feature extraction methods within the selected papers, categorised based on a baseline network, where the 'original' column signifies a new method.