# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Citation** & **Feature Extraction Technique** & **Description of the Feature Extraction Technique** \\ \hline [PERSON] et al. [50] & Pre-trained CNN (InceptionV3) with LSTM & A pre-trained InceptionV3 CNN is used for feature extraction and LSTM for temporal analysis of 20 frames. \\ \hline [PERSON] et al. [51] & Xception CNN with double-layer LSTM & Utilises Xception architecture for extracting spatial, frequency, and Pattern of Local Gravitational Force (PLGF) features from facial images. \\ \hline [PERSON], [PERSON], and [PERSON] [52] & Long-term recurrent CNN (LRCRN) with LSTM & Features are extracted using VGG-16 LRCN to find the temporal correlation between frames to detect the duration of the eyes remaining open. \\ \hline [PERSON] et al. [53] & Facial geometry landmarks module (FGLM) and U-Net & An FGLM acts as an encoder to extract facial landmark information, followed by feature map extraction using a U-Net network. \\ \hline [PERSON] et al. [54] & CNN-LSTM & Utilises a CNN model where pooling layers extract horizontal and diagonal edge features, and the fully connected layers map these features to the final output before inputting them into an LSTM network. \\ \hline [PERSON] et al. [14] & Pointwise CNN with bi-LSTM & A bidirectional LSTM processes facial sequences, followed by feature extraction using a pre-trained ImageNet network. \\ \hline [PERSON] et al. [38] & CNN (VGG-16) wrapped in a time-distributed layer & Utilises a time-distributed layer to wrap the VGG-16 encoder, generating an equal number of feature matrices to the processed frames, using one layer to learn from different frames. \\ \hline [PERSON], [PERSON], and [PERSON] [55] & Dense-Swish-Net-121 feature descriptor & Proposes a feature descriptor that integrates convolutional layers, dense blocks, and transitional layers with a bi-LSTM approach. \\ \hline [PERSON] et al. [56] & Pre-trained CNN (Xception) with LSTM & Uses an Xception CNN for feature extraction using the 2048 dimensional feature vectors after the last pooling layer as sequential LSTM input. \\ \hline [PERSON], [PERSON], and [PERSON] [57] & Pre-trained CNN (ResNeXt) architecture & Frame-level characteristics are extracted using a ResNeXt CNN model for feature extraction. \\ \hline [PERSON] et al. [58] & Pre-trained CNN (EfficientNetB7) with LSTM & This experiment uses an EfficientNetB7 baseline accompanied by one LSTM layer to leverage temporal and sequential information. \\ \hline [PERSON] et al. [59] & Pre-trained CNN (VGG-16) with LSTM & Utilises a pre-trained VGG-16 CNN to extract spatial information for each eye, followed by an LSTM for temporal feature extraction from the frame sequence. \\ \hline [PERSON] et al. [60] & Multi-stream CNN (EfficientNet-B3) with LSTM & Utilises a two-stream network to produce pairwise outputs before extracting features using a time-distributed EfficientNet-B3 CNN and LSTM. \\ \hline [PERSON] et al. [61] & Optical flow analysis with CNN-LSTM & The optical flow for pairs of frames, represented as a three-channel image indicating magnitude and direction of motion, is used as input to a CNN-LSTM model with two LSTM layers following convolutional layers for temporal analysis. \\ \hline [PERSON] et al. [25] & Pre-trained CNN (ResNeXt-50) with LSTM & ResNeXt-50 CNN retrieves the features from the frames, and after the final pooling layer, LSTM is the next sequential layer with 0.4 dropout. \\ \hline [PERSON], [PERSON], and [PERSON] [62] & Pre-trained CNN (ResNeXt-50) with LSTM & Employs a ResNeXt-50 CNN followed by a single layer of LSTM for feature extraction and temporal analysis of frames. \\ \hline [PERSON] et al. [63] & MobileNet-Single shot detection (SSD) & Integrates MobileNet with SSD, a feed-forward convolutional network for object detection, to extract feature maps and apply convolutional filters. \\ \hline [PERSON] [64] & CNN (ResNeXt-50)—GAN with LSTM & A GAN comprises a generator and discriminator, trained with a ResNeXt-50 CNN and LSTM to classify videos. \\ \hline \hline \end{tabular}
\end{table}