# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Citation**} & **LSTM** & \multirow{2}{*}{**Training Rate**} & \multirow{2}{*}{**Frame Rate**} & \multirow{2}{*}{**Frame Quality**} & \multirow{2}{*}{**Facial Landmark**} \\  & **Architecture** & & & & \\ \hline [PERSON] et al. [31] & x & ✓ & x & x & x \\ [PERSON] and [PERSON] [32] & x & x & ✓ & x & x \\ [PERSON] and [PERSON] [33] & ✓ & x & x & x & x \\ [PERSON] and [PERSON] [34] & x & x & x & ✓ & x \\ [PERSON] et al. [35] & x & x & x & x & x \\ [PERSON], [PERSON], and [PERSON] [36] & x & x & ✓ & x & x \\ [PERSON] et al. [37] & x & x & x & x & ✓ \\ [PERSON] et al. [2] & ✓ & x & x & x & x \\ [PERSON], [PERSON], and [PERSON] [38] & x & x & x & ✓ & x \\ [PERSON] et al. [39] & ✓ & x & x & x & x \\ [PERSON] and [PERSON] [40] & x & x & ✓ & x & x \\ [PERSON] et al. [41] & x & x & x & x & ✓ \\ [PERSON] et al. [43] & x & x & ✓ & x & x \\ [PERSON] [44] & x & x & ✓ & x & x \\ [PERSON] and [PERSON] [45] & x & x & ✓ & x & x \\ [PERSON] et al. [47] & x & x & ✓ & x & x \\ [PERSON], [PERSON], and & x & x & ✓ & x & x \\ [PERSON] [48] & x & x & x & ✓ & ✓ \\ [PERSON] et al. [50] & x & x & x & ✓ & x \\ [PERSON] et al. [51] & ✓ & x & x & x & x \\ [PERSON], [PERSON], and [PERSON] [52] & x & x & x & x & ✓ \\ [PERSON] et al. [53] & x & x & x & x & ✓ \\ [PERSON] et al. [54] & x & ✓ & x & x & x \\ [PERSON] et al. [14] & x & x & x & ✓ & x \\ [PERSON] et al. [38] & ✓ & x & x & x & x \\ [PERSON], [PERSON], and [PERSON] [55] & ✓ & x & x & x & x \\ [PERSON] et al. [56] & x & x & ✓ & x & x \\ [PERSON], [PERSON], and [PERSON] [57] & x & ✓ & x & x & x \\ [PERSON] et al. [58] & x & ✓ & x & x & x \\ [PERSON] et al. [59] & x & ✓ & x & x & x \\ [PERSON] et al. [61] & x & x & ✓ & x & x \\ [PERSON] et al. [25] & x & ✓ & ✓ & x & x \\ [PERSON], [PERSON], and & & x & ✓ & x & x \\ [PERSON] [62] & x & x & ✓ & x & x \\ [PERSON] et al. [63] & x & x & ✓ & x & x \\ [PERSON] [64] & x & ✓ & x & x & x \\ [PERSON] and [PERSON] [65] & x & x & x & x & ✓ \\ [PERSON] et al. [2021] [66] & x & x & ✓ & x & x \\ [PERSON] et al. [68] & x & x & ✓ & x & x \\ [PERSON] [69] & x & x & x & ✓ & ✓ \\ [PERSON] et al. [70] & x & ✓ & x & x & x \\ [PERSON] et al. [71] & x & x & ✓ & x & x \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Factors that influence video deepfake detection accuracy. Some researchers utilise the Half Total Error Rate (HTER), which is the average value of the false alarm rate and missed detection rate under a decision threshold. A lower HTER score indicates a stronger model performance. Similarly, the detection cost function (DFC) is a weighted sum of the missed detection rate and false alarm rate [50]. Using a range of measures to evaluate performance can provide a more nuanced understanding of the model's abilities to classify the data correctly. However, using multiple metrics can make direct comparisons between models more challenging as they determine different things. For example, AUC measures a model's sensitivity and specificity, but accuracy does not distinguish between these variables. Furthermore, accuracy is a weak measure for imbalanced datasets. Table 7 compares the performance of the selected models, with a comparison to a CNN model alone if provided by the author. The dataset used during testing, the performance metric, and the network backbone are provided. Where a range of backbones were tested, the highest performance metric is given. The frame rate also produced standardisation problems when incorporating LSTM, as some frames were reported as a sequence length whilst others were reported as 'frames per second', which was defaulted to if available.