# 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 c} \hline \hline
**Citation** & **Backbone** & **CNN** & **CNN-LSTM** & **Metric** & **Dataset Used** & **Frame Rate** \\ \hline [PERSON] et al. [31] & Original & - & 97.40\% & ACC & Celeb-DF & - \\  & & - & 94.47\% & & DFDC & - \\ \hline Al-Dhabi and & & & 95.54\% & ACC & FF++, DFDC, & \\ [PERSON] [32] & ResNeXt & - & 95.54\% & ACC & Celeb-DF & 80 \\ \hline Al-Dulaimi and & & & 98.24\% & ACC & DFDC & - \\ [PERSON] [33] & Original & - & 98.24\% & ACC & FF++ (lossless) & - \\ \hline Amerini and & & 91.72\% & 94.29\% & ACC & FF++ (lossless) & - \\ [PERSON] [34] & Original & 79.41\% & 85.27\% & ACC & FF++ (c23) & - \\ \hline [PERSON], [PERSON], and [PERSON] [36] & & - & 99\% & ACC & FF++ & \\  & & - & 99.93\% & ACC & Celeb-DF & 30 \\  & & - & 92.43\% & ACC & DFDC & \\ \hline [PERSON] et al. [37] & ResNeXt & - & 98\% & ACC & FF++, DFDC, & 150 \\  & & 96.71\% & 100\% & ACC & FF++ & - \\ \hline [PERSON] et al. [2] & Xception & 89.91\% & 99.16\% & ACC & Celeb-DF & - \\ \hline [PERSON], [PERSON], and & & & & & & \\ Ahsan [38] & ResNet & 70\% & 82.4\% & ACC & DFDC & 150 \\ \hline [PERSON] et al. [39] & InceptionNet & - & 84.26\% & ACC & DFDC & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of selected publications. Figure 9: Confusion matrix. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
Numerous results indicate that the combined CNN-LSTM architecture can outperform CNN-only models. [PERSON] and [PERSON] [34] found that the classification accuracy was highest (94.29%) when using an LSTM-based model compared to 91.72% for a CNN-only model, demonstrating the benefits of exploiting sequence correlations across frames. Similarly, [PERSON], [PERSON], and [PERSON] [36] found that combining a spatial attention mechanism with an Xception backbone and ConvLSTM achieved an impressive 0.99 AUC score, outperforming the Xception CNN without LSTM, which achieved 70.10% accuracy [79]. Additionally, the C-LSTM model from [PERSON], [PERSON], and [PERSON] [48] outperforms MesoNet [87] and CNN state-of-the-art models, achieving an accuracy of 98.21% in a lower training time than the CNN model whilst also having the lowest loss value. The findings of [PERSON] et al. [38] combining wide ResNet architecture and LSTM achieved the same accuracy as ResNet alone on frames of a 256 \(\times\) 256 resolution but performed 9% better when 512 \(\times\) 512 resolution frames were used as input and 12% better on the DFDC test set, achieving 82.4%. The results from [PERSON] et al. [14] achieved an AUC score of 81.53 on the FF++ with strong compression (c40) enabled, while achieving 76.04 without LSTM, therefore demonstrating that LSTM improves detection when other fine-tuning methods are added, including updating rate per layer and adding dropout. In particular, almost all frame-level performance improved for the medium compression (c23) videos, increasing the video-level AUC score from 92% to 99% on the overall FF++ dataset. Achieving a 97.26 AUC score using an EfficientNet CNN-LSTM architecture with transfer learning on the DFDC dataset, the results of [PERSON] and [PERSON] [67] slightly improve the 97.18% AUC score achieved by the DFDC competition winner [88], who did not utilise LSTM in their EfficientNet CNN implementation. [PERSON] et al. [61] compare the performance of the base models with optical flow, LSTM, and CNN on the Celeb-DF, DFDC, and FF++ datasets. When optical flow and LSTM are combined, the model cannot differentiate well and produces its worst performance with an average AUC score of 0.5 on all datasets; therefore, the classifier cannot distinguish between positive and negative class points effectively. When using optical flow as input to the CNN and training without LSTM, the model performs better on the Celeb-DF and DFDC datasets with 0.83 and 0.68 AUC scores, respectively. However, combining optical flow, LSTM, and CNN produces the strongest performance for the FF++ dataset with an AUC score of 0.91.