# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
\begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Citation** & **Summary of Contribution** & **Limitations** \\ \hline [PERSON] et al. [70] & The paper uses transfer learning using InceptionResNetV2 CNN for feature extraction and an LSTM layer, where training for 40 epochs improved accuracy by 6.73\% over 20 epochs. & The model’s input is the deepfake portion of clip; therefore, it is not representative of deepfake videos where a real video feed is used. \\ \hline [PERSON] et al. [71] & The paper presents a novel ResNeXt-50-32x4d and LSTM-based deepfake detection model achieving 88.54\% accuracy on the DFDC dataset, setting a new benchmark for performance. & The model only considers the first 150 frames as input, significantly reducing its practical usability for analysing longer videos. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._LSTM in models [25; 32; 43; 45; 57; 62; 64; 68; 71; 76] indicates its robustness as a baseline model for feature extraction. However, there are narrow architectural differences that present opportunities for comparison. For example, [PERSON] et al. [68] employ three LSTM layers and ReLU activation, diverging from the single LSTM layer and SoftMax activation used by others, introducing a deeper hierarchical representation of temporal dependencies. Similarly, [PERSON] and [PERSON] [45] replace the SoftMax activation function with ReLU within the LSTM layer and use triplet loss detection to calculate the differences between two real images and one fake before updating the weights to reduce loss. These variations suggest there are different strategies for enhancing model performance and generalisation whilst using the same backbone network. Additionally, except for [PERSON] et al. [43] who use 0.5, the consistent use of a dropout rate of 0.4 at the LSTM layer across all studies emphasises its importance as a regularisation technique to prevent overfitting. Other frequently used feature extraction techniques are the Xception and Inception networks. Interesting applications of the baseline for feature extraction include using an Xception network to extract the spatial, frequency, and Pattern of Local Gravitational Force (PLGF) features of the facial images [51]. The features are then spliced and fused to obtain 6144 dimensional features of frames that are used as input into a double-layer LSTM. [PERSON] and [PERSON] [65] also utilise a double-layer LSTM with Xception to extract the features of the mouth, eyes, and nose facial regions separately. Alternatively, variations in inception networks are used in studies [39; 40; 50; 70] before passing feature vectors to LSTM cells, but at the cost of computational complexity. Based on the residual blocks used in MobileNetV2, EfficientNet uses fixed compound scaling to scale all dimensions of depth, width, and resolution. The usage of EfficientNet for feature extraction varies throughout the studies. [PERSON] and [PERSON] [67] use EfficientNet-B0 and then normalise the features before the feature vectors are passed to the LSTM. [PERSON] et al. [60] utilise EfficientNet-B3 within a two-stream network for pairwise frame comparison, emphasising its effectiveness as a baseline network as the model learns features independent of the deepfake generation method, although this can be accredited to its use of contrastive loss to reduce interclass variations. [PERSON] et al. [66] extract faces using an MTCNN but extract the feature vectors using EfficientNet-B5 whilst a soft-attention mechanism is applied in conjunction to determine which of the feature vectors are the most significant. [PERSON] et al. [58] utilise EfficientNet-B7 with a single LSTM layer to leverage sequential data but it struggles with overfitting. Comparative analysis of the results from [PERSON] and [PERSON] [67] reveal a significantly reduced training time of EfficientNet architecture compared to other techniques, highlighting its potential for real-time video deepfake detection deployment.