# 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
**Author** & **Challenge** & **Description of the Challenge** \\ \hline [PERSON] [44] & Computationally expensive & This model has a trade-off between accuracy and computational efficiency \\ \hline [PERSON] and [PERSON] [45] & Frame rate limitation & This model is limited to accepting a frame rate of 100 fps due to the computational expense. \\ \hline [PERSON] et al. [46] & Increased data pre-processing & Due to the complexity of the feature extraction stage, the data are heavily pre-processed and then re-processed manually, which requires a lot of extra time and input. \\ \hline [PERSON] et al. [47] & Diminishing returns & After 100 frames, the model’s performance plateaus. \\ \hline [PERSON], [PERSON], and [PERSON] [48] & Facial rotations limiting detection & The video subject looking away causes detection to be compromised. \\ \hline [PERSON] and [PERSON] [49] & Loss of spatial information and lack of interpretability & Applying anisotropic diffusion can lead to the loss of fine spatial details whilst making it difficult to distinguish which specific features are affected by the diffusion. \\ \hline [PERSON] et al. [50] & Failure to detect online content & This model could not detect content collected from YouTube videos, which hosts a large portion of deepfake content. \\ \hline [PERSON] et al. [51] & Complexity & The involvement of spatial, frequency, time domain feature extraction, and LSTM is complex and computationally expensive. \\ \hline [PERSON], [PERSON], and [PERSON] [52] & Subjective threshold & The threshold set to what determines an open or closed eye impacts detection. \\ \hline [PERSON] et al. [53] & Reliance on Facial Geometry Prior Module (FGPM) & Relying on FPGM to extract facial landmarks may be ineffective in detecting deepfake techniques such as attribute manipulation, where changes in features such as hair, colours, and skin retouching do not affect the facial structure. \\ \hline [PERSON] et al. [54] & Pre-processing is sensitive to illumination and occlusions & The usage of the Viola–Jones algorithm is sensitive to occlusions and under/over light exposure. \\ \hline [PERSON] et al. [14] & Facial occlusions and poor illumination & Failure to detect real faces deduced from poor illumination and a lack of facial hair in training sets can impact results from test sets where this is present. \\ \hline [PERSON] et al. [38] & Poor illumination within the frame & The model struggled with low lighting, obscurity, and shadows. \\ \hline [PERSON], [PERSON], and [PERSON] [55] & ReLU activation function limitations & The ReLU activation function is liable to remove negative values during computation, which can potentially result in the loss of some subtle visual characteristics in the deeper layers. \\ \hline [PERSON] et al. [56] & Generalisability & The model is trained on a very small dataset, limiting its applicability to unseen data. \\ \hline [PERSON], [PERSON], and [PERSON] [57] & Discarding frames & This model only uses the first 150 sequential frames, which could discard the detection of a deepfake in the later portion of the clip. \\ \hline [PERSON] et al. [58] & Overfitting & The model’s testing accuracy is lower than its training and validation accuracy, therefore indicative of overfitting. \\ \hline [PERSON] et al. [59] & Low detection accuracy on non-GAN generated deepfakes & Deepfakes generated using neural texture architecture had a low detection accuracy. \\ \hline [PERSON] et al. [60] & Low detection accuracy for attribute manipulation & The model struggled to detect deepfakes where the expressions were swapped. \\ \hline [PERSON] et al. [61] & Frame rate limitation & A frame rate input of 20 fps produced a 0.5 accuracy measure. \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._To overcome these challenges, we propose several solutions, which are detailed and discussed in Table 9.