# 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] and [PERSON] [34] & Prediction error with convolutional layer and LSTM-RNN ensemble & A bounding box around the face (256 \(\times\) 256 pixels) is used as input, which is extracted from select video frames. Utilises prediction to detect deepfake faces. \\ \hline [PERSON] et al. [35] & Triplet LSTM-RNN autoencoder with VGGNet-16 backbone & Utilises a triplet LSTM-RNN autoencoder in conjunction with a VGGNet-16 backbone to capture spatial information within each frame and temporal relationships. \\ \hline [PERSON], [PERSON], and [PERSON] [36] & Spatiotemporal attention mechanism with ConvLSTM and Xception CNN & Integrates a spatiotemporal attention mechanism to capture correlations between frames. The video is processed through Xception architecture before ConvLSTM is employed to extract spatiotemporal inconsistency features. \\ \hline [PERSON] et al. [37] & RNN-CNN-LSTM hybrid model & Utilises an RNN model trained on 150 frames per video after pre-processing to ensure frame consistency. Frame-level characteristics are extracted using a CNN (ResNeXt), followed by an RNN-LSTM to determine video authenticity. \\ \hline [PERSON] et al. [2] & Xception CNN and bidirectional LSTM fusion with Python Dlib & Employs Dlib to locate facial regions within frames followed by linear smoothing filters. Utilises an Xception CNN to extract vector representations of facial regions from frames. \\ \hline [PERSON], [PERSON], and [PERSON] [38] & Wide ResNet-CNN and LSTM attention layer & A pre-trained wide ResNet architecture is used for feature extraction and an LSTM layer to capture temporal data. Data are then input into a multi-head attention layer, so the model selects essential features. \\ \hline [PERSON] et al. [39] & InceptionV3 for textural feature extraction & Utilises InceptionV3 CNN pre-trained on the ImageNet dataset for textural feature extraction and implements a separate CNN for emotion feature extraction. \\ \hline [PERSON] and [PERSON] [40] & Pre-trained CNN (InceptionV3) with LSTM & Each frame’s features are extracted using the InceptionV3 model and the feature vectors are used as input into the LSTM. \\ \hline [PERSON] et al. [41] & Transfer learning with ResNet CNN and LSTM & Employs a pre-trained ResNet CNN where subsequent layers are removed to create a dedicated feature extractor network, which is input into an LSTM. \\ \hline [PERSON] et al. [42] & Hybrid CNN-LSTM-GRU model with Gaussian blur pre-processing. The extracted features are then fed into a multilayer LSTM-GRU model. \\ \hline [PERSON] et al. [43] & Pre-trained CNN (ResNeXt) with LSTM & Adopts a 50-layer ResNeXt CNN architecture for feature extraction. The output of this CNN is a feature vector, which is passed into an LSTM network. \\ \hline [PERSON] [44] & Pre-trained CNN (ResNeXt) with LSTM & Utilises ResNeXt-50, analysing the first 150 frames of each video and a 2048 dimensional LSTM. \\ \hline [PERSON] and [PERSON] [45] & ResNeXt CNN with deep triplet loss & ResNeXt architecture extracts temporal information whilst a deep model compares triplet images to distinguish between real or deepfake images. \\ \hline [PERSON] et al. [46] & ResNet18 CNN with LSTM and Recycle-GAN fusion & Employs ResNet18 CNN followed by an LSTM layer and a Recycle-GAN two stream fusion to detect spatial-temporal inconsistencies. \\ \hline [PERSON] et al. [47] & Pre-trained CNN (ResNeXt) with LSTM & Utilises ResNeXt-50 architecture with LSTM with an input of 150 frames for extraction. \\ \hline [PERSON], [PERSON], and [PERSON] [48] & CNN-LSTM & Utilises three convolutional layers and three max pooling layers for high-level feature extraction from each sequential video frame. The output is flattened and used as input to an LSTM. \\ \hline [PERSON] and [PERSON] [49] & Nonlinear anisotropic diffusion pre-processing with CNN & Applies nonlinear anisotropic diffusion to the frames of each input sequence for noise reduction, which is then fed into a CNN to capture spatial information, generating an output of 50 features per frame. \\ \hline \hline \end{tabular}
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