# 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** & **Summary of Contribution** & **Limitations** \\ \hline [PERSON] et al. [31] & The paper proposes a model that combines CNN with LSTM architecture, where the weight and bias values are initialised using Particle Swarm Optimisation (PSO), which is finetuned on a validation set to obtain the optimal parameters for performance. & The failure of accuracy to improve with increased iterations points to a limitation in the training process or model architecture. \\ \hline [PERSON] and [PERSON] [32] & The paper proposes a model that uses ResNeXt-50 with LSTM and bridges the gap between training and test accuracy on sequential frames of a video using different datasets, with an extremely low training loss of 0.0053. & The proposed model has a low accuracy as the frame rate input is reduced, diminishing its effectiveness. \\ \hline [PERSON] and [PERSON] [33] & The paper introduces a hybrid CNN-LSTM model for deepfake image detection, achieving 98.21\% precision on benchmark datasets (DPDC, Ciplab). This approach effectively combines spatial and temporal analysis to improve deepfake identification accuracy, addressing a critical concern in digital media & The proposed model’s 0.26\% error rate highlights the inherent difficulty of deepfake detection; further research is needed to improve robustness and generalisability. \\ \hline [PERSON] and [PERSON] [34] & The paper proposes a theoretical framework that enables proof of authenticity using LSTM as a deep encoder for creating unique discriminative features, which are compressed and hashed into a transaction. The content is validated and can then be traced back with a label verifying its authenticity as not deepfake. \\ \hline [PERSON], [PERSON], and [PERSON] [36] & The paper introduces a spatiotemporal attention mechanism using ConvLSTM to address the difficulties presented by Xception-LSTM-based algorithms in identifying deepfake videos. & The model runs the risk of overfitting and reduced generalisability from an increase in model complexity. \\ \hline [PERSON] et al. [37] & The paper proposes an RNN model that is trained on 150 frames per video to check for frame consistencies before using CNN ResNet architecture to retrieve frame-level characteristics, followed by RNN-LSTM to determine video authenticity. & The proposed model is trained on a high frame rate, reducing its generalisability to lower frame rate deepfake content. \\ \hline [PERSON] et al. [2] & The paper uses a CNN to obtain a vector representation of the facial region within a frame before passing a sequence of frames to a bidirectional LSTM cell. The spatial features from the XceptionNet module are passed into bidirectional LSTM cells with a double pass to give 99.7\% accuracy on the FF++ dataset. \\ \hline [PERSON], [PERSON], and [PERSON] [38] & The paper proposes a model that uses a Wide CNN ResNet architecture with LSTM before inputting data into a multi-head attention layer, so the model selects essential features for stronger performance on video classification. & The generalisation of the findings to a broader range of deepfake manipulations is limited as the study focuses on a specific transfer learning approach. \\ \hline [PERSON] et al. [39] & The paper proposes an InceptionNetV3 CNN with LSTM that shows that emotional analysis is a robust method of deepfake detection, aiming to identify how visual emotional artefacts in deepfake videos disrupt temporal coherence. & Facial expression and representation of emotion can vary amongst contexts and cultures, therefore making a non-biased baseline hard to establish. \\ \hline [PERSON] and [PERSON] [40] & The paper proposes combining InceptionV3 CNN with LSTM for sequence processing without loss functions and performs with 97\% accuracy on 40-80 frames. & Where a face is not fully present, such as a facial rotation, the model has a low accuracy of 40\%. \\ \hline \hline \end{tabular}
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