# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Citation** & **ResNet** & **ResNetXt** & **Xception** & **InceptionNet** & **VGG-16** & **EfficientNet** & **MobileNet** & **Image** & **ConvLSTM** & **Original** \\ \hline [PERSON] et al. (2018) & x & x & x & x & x & x & x & x & x & ✓ \\ [PERSON] and [PERSON] (2018) & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] and [PERSON] (2018) & x & x & x & x & x & x & x & x & ✓ \\ [PERSON] and & x & x & x & x & x & x & x & x & x & ✓ \\ [PERSON] (2018) & x & x & x & x & x & x & x & x & x & ✓ \\ [PERSON] et al. (2018) & x & x & x & x & ✓ & x & x & x & x & ✓ \\ [PERSON], [PERSON], and [PERSON] (2018) & x & x & x & x & x & x & x & x & ✓ & x \\ [PERSON] et al. (2018) & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] et al. (2018) & x & x & ✓ & x & x & x & x & x & ✓ & x \\ [PERSON], [PERSON], and & & & & & & & & & & & \\ [PERSON] (2018) & ✓ & x & x & x & x & x & x & x & x & x \\ [PERSON] et al. (2018) & x & x & x & ✓ & x & x & x & x & x & x \\ [PERSON] and [PERSON] (2018) & x & x & x & ✓ & x & x & x & x & x & x \\ [PERSON] et al. (2018) & ✓ & x & x & x & x & x & x & x & ✓ & x \\ [PERSON] et al. (2018) & x & x & x & x & x & x & x & x & ✓ & x \\ [PERSON] et al. (2018) & x & ✓ & x & x & x & x & x & x & ✓ & x \\ [PERSON] et al. (2018) & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] (2018) & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] and [PERSON] (2018) & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] et al. (2018) & ✓ & x & x & x & x & x & x & x & x & x \\ [PERSON] et al. (2018) & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON], [PERSON], and & & & & & & & & & & & \\ [PERSON] (2018) & x & x & x & x & x & x & x & x & ✓ & \\ [PERSON] and & & & & & & & & & & & \\ [PERSON] (2018) & x & x & x & ✓ & x & x & x & x & ✓ & ✓ & x \\ [PERSON] et al. (2018) & x & x & x & ✓ & x & x & x & x & x & x & x \\ [PERSON] et al. (2018) & x & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON], [PERSON], and & & & & & & & & & & & & \\ [PERSON] (2018) & x & x & x & x & ✓ & x & x & x & x & x & ✓ \\ [PERSON] et al. (2018) & x & x & x & x & x & x & x & x & x & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] et al. (2018) & x & x & x & x & x & x & x & x & x & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Feature extraction techniques used in the selected publications. **RQ3: What are the most common datasets used in the implementation of video deepfake detection tools that utilise CNN with LSTM? **  
Datasets play a crucial role in training ML models to identify altered or synthesised content. High-quality and diverse datasets are essential because deepfake videos often involve subtle manipulations that can easily deceive the human eye. These manipulations may include alterations in facial expressions, speech, or body movements, which are challenging to detect without robust data-driven models. By exposing detection algorithms to a wide range of fake and authentic videos, the models can learn to differentiate between genuine and manipulated content, improving the overall accuracy of deepfake detection. The importance of datasets also extends to their ability to generalise across different types of deepfakes [77]. As deepfake generation techniques evolve, new and more sophisticated methods emerge, making it vital for detection models to remain adaptable. Datasets that capture diverse deepfake techniques allow for a more comprehensive training process, enabling the model to detect deepfakes created using various algorithms and approaches. This adaptability is critical in the real-world application of these technologies, where deepfakes may be used in malicious contexts such as misinformation, identity fraud, or cybercrime. Moreover, the continuous expansion and updating of datasets ensure that deepfake detection systems stay relevant and effective. As deepfake creation methods become more advanced, detection systems must keep pace by learning from up-to-date datasets. Figure 7 illustrates the frequency of the common datasets used across the literature.