# 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 \multirow{4}{*}{[PERSON] et al. [51]} & The paper proposes a feature fusion detection model where feature extraction from spatial, temporal, and frequency domains occur at the same time. The results show that the error rate is higher without feature fusion or LSTM. & The proposed model lacks generalisability to datasets it was not trained on, indicating a high variance and low bias in its predictive capabilities. \\ \hline \multirow{4}{*}{[PERSON], [PERSON], and [PERSON] [52]} & In the paper, a long-term recurrent CNN is employed with LSTM to detect deepfakes using a lack of blinking by detecting the number of frames an eye is open and its temporal correlation to its preceding frame. The results conclude that deepfake videos have a blinking rate ten times lower than real videos. & The threshold set to determine an open or closed eye impacts detection, affecting the precision and recall of the overall eye-state classification system. \\ \hline \multirow{4}{*}{[PERSON] et al. [53]} & The paper proposes a model that combines facial geometry feature maps with CNN-LSTMs to decode and detect manipulations at a pixel-wise level. The model outperforms XceptionNet and CNN-based models on a range of datasets. & The proposed model is not robust as it relies on facial standards, which can change depending on expression or if something’s blocking the view. \\ \hline \multirow{4}{*}{[PERSON] et al. [54]} & The paper demonstrates that a CNN with LSTM requires a larger training set than a testing set compared to standalone CNN, with higher accuracy detected on the FF++ dataset with an 80/20 split than when a 70/30 split is used, outperforming previous works. & The proposed model has a low ability to detect real faces that have facial hair or are poorly illuminated, resulting in a high rate of false negatives and reduced overall accuracy. \\ \hline \multirow{4}{*}{[PERSON] et al. [38]} & The paper proposes a lightweight VGG16 CNN-LSTM that is 152 times smaller than existing methods, significantly improving computational efficiency and reducing resource requirements. & Adding more than 128 LSTM layer units led to overfitting and reduced performance on the validation test set. \\ \hline \multirow{4}{*}{[PERSON], [PERSON], and [PERSON] [55]} & The paper introduces a new feature descriptor named Dense-Swish-121, which integrates convolutional layers, dense blocks, and transitional layers with a bi-LSTM approach. The model outperforms five CNN-LSTM models on the DFDC dataset, reaching 99.3\% accuracy with Dense-Swish-Net121 despite having fewer parameters. & The choice of the ReLU activation function limited the model’s ability to detect subtle visual cues, resulting in reduced detection accuracy. \\ \hline \multirow{4}{*}{[PERSON] et al. [56]} & The paper uses XceptionNet with LSTM, achieving a significant reduction in computational time compared to existing methods, while maintaining comparable or even superior accuracy & There is a large discrepancy and fluctuations between training and validation accuracy, indicating overfitting. \\ \hline \multirow{4}{*}{[PERSON], [PERSON], and [PERSON] [57]} & The paper uses ResNeXt-50-32x4d with LSTM to determine whether a video is a deepfake or real with 85\% accuracy using only 10 frames, which is equivalent to less than a second. & The proposed model is computationally expensive to analyse 300 frames of a video. \\ \hline \multirow{4}{*}{[PERSON] et al. [58]} & The paper compares baselines and concludes that EfficientNetB7 is the most effective CNN model for detecting whether a video is real or deepfake using binary classification. & The model’s testing accuracy is lower than its training and validation accuracy, which is therefore indicative of overfitting. \\ \hline \multirow{4}{*}{[PERSON] et al. [59]} & The paper detects deepfake content using eye-blinking patterns, where a lack of, or too frequent, blinking can indicate deepfake content. Using a batch size of 20 with 105 epochs, the model’s optimal results occurred at 95.57\% on the FF+ dataset. & The proposed model poorly detects neural texture deepfakes, resulting in a higher rate of missed detections for this specific type of deepfake. \\ \hline \hline \end{tabular}
\end{table}