# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
When detecting specific facial landmarks, such as the eyes, using a standalone CN to train the pixel pattern of the eyes is generally unable to determine whether the eyes are in an \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Citation** & **Backbone** & **CNN** & **CNN-LSTM** & **Metric** & **Dataset Used** & **Frame Rate** \\ \hline [PERSON] et al. [63] & MobileNet & - & 97.63\% & ACC & DFDC & 30 \\ \hline [PERSON] and [PERSON] [65] & Xception & 99.40 & 99.95 & AUC & FF++ & 60 \\  [65] & 83.60 & 97.06 & AUC & Celeb-DF & 60 \\ \hline [PERSON] et al. [86] & EfficientNet & - & 99.57\% & ACC & FF++ & 15 \\ \hline [PERSON] and [PERSON] [67] & EfficientNet & 98.69\% & 97.56\% & ACC & DFDC & 20 \\  & 85.84\% & 81.23\% & ACC & FF++ & 20 \\ \hline  & & - & 97.89\% & ACC & FF++ & \\  & & - & 97.79\% & ACC & Celeb-DF & 100 \\  & & - & 97.75\% & ACC & DFDC & \\  & & - & 93.29\% & ACC & FF++, Celeb-DF, & \\  & & - & 93.29\% & ACC & DFDC & \\ \hline [PERSON] [69] & ResNet & - & 93.23\% & ACC & UADFV (real) & 28 \\  & - & 98.30\% & ACC & UADFV(fake) & 28 \\ \hline [PERSON] et al. [70] & InceptionNet & - & 91.48\% & ACC & FF++, Celeb-DF, & - \\  & & & & & DFDC & 30 \\ \hline [PERSON] et al. [71] & ResNeXt & - & 88.55\% & ACC & DFDC & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._open or closed state; therefore, using an RNN for temporal features can improve the model. The results in this domain by [PERSON] et al. [59] achieved up to 95.57% accuracy. Other findings by [PERSON] et al. [52] reinforce this, as the LRCN performs at 0.99 AUC compared to 0.98 AUC using a CNN, as the long-term dynamics improve the detection of the state. The studies discussed highlight the advantages of CNN-LSTM models over CNN-only approaches, particularly in exploiting temporal dependencies for improved accuracy. [PERSON] and [PERSON] [34] and [PERSON], [PERSON], and [PERSON] [36] both report higher classification accuracy and AUC scores when combining CNN with LSTM, demonstrating that LSTM enhances temporal feature learning across frames. Similarly, [PERSON], [PERSON], and [PERSON] [48] found that CNN-LSTM outperforms state-of-the-art CNN models with lower loss and faster training times. [PERSON] et al. [38] further emphasise that LSTM's performance gains are more significant with higher-resolution input data. However, [PERSON] et al. [61] show that while CNN-LSTM models perform well on specific datasets like FF++, combining LSTM with optical flow can reduce performance on others. [PERSON] et al. [59] and [PERSON] et al. [52] reinforce LSTM's strengths, particularly in tasks involving temporal features like facial recognition, where RNN-based models achieve superior results. Overall, CNN-LSTM models generally outperform CNN-only models but are dataset-dependent. **RQ6: What are the challenges involved in implementing video deepfake detection tools that utilise CNN with LSTM? **  
One of the key challenges in deepfake detection models that combine CNN and LSTM architectures is the trade-off between computational efficiency and model performance. [PERSON], [PERSON], and [PERSON] [36] found that their CNN-LSTM model had an increased runtime compared to other algorithms due to the non-parallelisable convolutional operations of ConvLSTM, resulting in a worst-case time-space complexity of O(\(n^{2}\)). This complexity implies that as the input size or model complexity grows, computational costs can increase quadratically. Similarly, other studies [37; 44; 63] highlighted the burden of large processing times when networks are required to learn from thousands of input images, creating a challenge in deploying these models in resource-constrained or real-time environments. Addressing this issue by optimising the architecture is essential to maintain a balance between performance and computational feasibility.