# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
This can be achieved by augmenting the training data with samples depicting various degrees of facial rotation. Furthermore, the CNN-LSTM architecture of the model should be designed to effectively track the dynamics of the face throughout the video sequence. The convolutional layers can be optimised to extract spatial features that are invariant to facial rotations, whilst the LSTM component learns the temporal patterns associated with real and deepfake manipulations in the presence of differing facial orientations to improve the overall robustness and real-life applications of video deepfake detection. [PERSON] et al. [38] deduced that the illumination within the frame can influence the detection accuracy. These findings highlight the requirement for the development of a new dataset that contains a range of lighting changes within the frames alongside a CNN-LSTM model that can learn under these conditions. A CNN model could be used to extract the spatial features from each frame, whilst the LSTM layers capture the temporal dependencies to allow the model to maintain a coherent understanding of the face and retain facial features that were present in the previous frames despite the lighting fluctuations. By aggregating the probabilities from multiple frames under different lighting conditions, the final computed classification can be more representative of dynamic conditions in which deepfake videos are often filmed. One challenge that emerges in deepfake detection is the performance plateau due to parameter tuning, as discussed by [PERSON] et al. [31]. In their research, they employed the PSO algorithm to finetune parameters iteratively, expecting continuous improvements. However, their findings showed that accuracy did not always increase as expected. This plateau suggests that certain optimisation algorithms might not be sufficient to boost performance after a certain threshold, highlighting the need for alternative methods or more sophisticated techniques to improve model tuning and avoid diminishing returns. The computational expense of deepfake detection models is another prominent challenge, as highlighted by [PERSON] and [PERSON] [33]. These models often require extensive resources to achieve high accuracy, resulting in trade-offs between computational efficiency and model performance. [PERSON], [PERSON], and [PERSON] [36] similarly reported that their model's non-parallelisable convolutional operations caused an increase in runtime, exacerbating the time-space complexity. These findings illustrate that the demand for computational power can impede the scalability and practical deployment of deepfake detection systems, particularly for real-time or large-scale applications. Also, generalisation against unseen deepfake techniques remains a persistent issue for CNN-LSTM models. [PERSON] et al. [14] concluded that the failure to detect real faces in their model could be accredited to characteristics such as a lack of facial hair in the training sets, which is a limitation when attribute manipulation is the deepfake creation method. This limitation in the training data can impact the model's performance on test sets where such features are present. When a small or undiversified dataset is used, the model learns patterns relating to specific facial characteristics, which can then cause real faces to be misclassified. Furthermore, [PERSON] et al. [53] explored the use of a Facial Geometry Prior Module (FGPM) to extract facial landmarks for deepfake detection before passing the input to the CNN-LSTM architecture. However, the researchers found that this approach may be ineffective in detecting deepfake techniques that involve attribute manipulation, such as changes to hair, colouring, and skin retouching, as these do not significantly affect the facial structure. The evolving nature of deepfake generation techniques, such as the emergence of the Neural Textures subset within the FF++ dataset, further highlights the need for more adaptable detection models.