# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion

Table 3: A summary of the contributions and limitations of each study of the selected publications. \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Citation** & **Summary of Contribution** & **Limitations** \\ \hline [PERSON] et al. [41] & The paper combines CNN for feature extraction and LSTM for storing feature vectors for frames instead of saving the frame itself to train large amounts of data without memory expense. & The proposed model’s feature extraction only detects key facial landmarks, not additional features such as noise or blur. \\ \hline [PERSON] et al. [42] & The paper proposes a hybrid approach to deepfake detection using GRU and LSTM showing that CNN has higher performance metrics than when using LSTM alone, with particular emphasis on placing the GRU layers before the LSTM layers on the DFDC dataset. & The proposed model uses the same dataset for training and testing, reducing generalisability. \\ \hline [PERSON] et al. [43] & The paper combines ResNeXt-50 for feature extraction with LSTM to train the model for classification whilst intentionally reducing the number of frames fed into the network, resulting in very high detection accuracy on the DFDC dataset. & The proposed model trains on 300 frames per video and uses the same dataset for training and testing, which is computationally expensive. \\ \hline [PERSON] [44] & The paper proposes a model that adopts ResNeXt-50-32\(\times\)4d architecture with LSTM dimensions of 2048 and 2048 hidden layers, concluding the architecture has stronger detection accuracy at 100 frames per video analysed in comparison to any value of frames below that. & The proposed model is computationally expensive, limiting its applicability where high-speed processing is required, despite achieving high accuracy. \\ \hline [PERSON] and [PERSON] [45] & The paper compares the performance accuracy of using temporal-based detection and triplet loss detection. A combination of a temporal model with a triplet deep model is created to obtain higher training and testing accuracy than standalone models. & The proposed model is limited to using a frame rate of 100 frames per second (fps), with higher frames per second improving accuracy. \\ \hline [PERSON] et al. [46] & The paper proposes a model that uses a CNN-LSTM followed by a Recycle-GAN with a two-stream fusion, merging the spatial and temporal data to evaluate the data whilst pushing its results back to the start of the network for continuous learning. & The proposed model failed to detect any neural textures in generated deepfakes, demonstrating a critical weakness. \\ \hline [PERSON] et al. [47] & The paper proposes a novel deepfake detection model using a ResNeXt-50 CNN and LSTM. Its key contribution is achieving 83.21\% accuracy while processing only 10 frames, significantly improving efficiency. & The diminishing returns after 100 frames significantly limit the model’s ability to leverage longer video sequences for improved accuracy. \\ \hline [PERSON], [PERSON], and [PERSON] [48] & The paper proposes a C-LSTM model that uses CNN with LSTM to detect face-swapped politicians and achieves an accuracy of 98.21\% for 0.99 precision and 0.93 recall values. & When the video subject consistently looks away, detection accuracy is compromised. \\ \hline [PERSON] and [PERSON] [49] & The paper proposes a CNN-LSTM model without a pre-processing stage, applying nonlinear diffusion to enhance liveness detection on video sequences as the CNN captures spatial information and the features obtained by the LSTM layer are classified as real or fake with a test accuracy of 96\%. \\ \hline [PERSON] et al. [50] & The paper proposes a maximal figure-of-merit (MFoM) framework, which uses the detection cost function as its performance measure and the equal error rate (EER) to optimise measures of performance and view video deepfake detection as a cost-sensitive objective. The model then follows a CNN-LSTM-based approach to determine a reduction in the EER of detection. \\ \hline \hline \end{tabular}
\end{table}