# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
A well-tuned training rate ensures that the model converges at an optimal pace, striking a balance between learning from the data and avoiding overfitting. If the rate is too high, the model may miss important nuances and fail to capture subtle differences between real and fake video frames, leading to poor detection accuracy. Conversely, if the rate is too low, the model may converge too slowly or get stuck in local minima, resulting in underperformance. Therefore,finding the optimal training rate is essential for improving the model's accuracy, as it allows the network to efficiently learn the complex patterns and temporal dependencies needed to distinguish between genuine and deepfake videos. The training-test split and the number of epochs used during training were found to impact the detection accuracy of the LSTM-CNN models. An epoch is a complete pass of the algorithm through an entire training set, whilst a batch size is the number of samples processed before the model updates [85]. [PERSON], [PERSON], and [PERSON] [57] observed that as the number of epochs and frame sequences increased, the model's accuracy improved while the training loss decreased. Similar results by [PERSON] [64] found that increasing the number of epochs from 5 to 13 led to a boost in accuracy, from 91.35% to 97.25%. Findings from [PERSON] et al. [25] illustrated that the LSTM-based model showed higher accuracy when trained for 40 epochs (90.37%) compared to 20 epochs (84.18%), though there was a fairly high false negative rate in both cases. Further research from [PERSON] et al. [70] confirmed this finding, with the 40-epoch model achieving 91.48% accuracy versus 84.75% for the 20-epoch model. This demonstrates that more training time allows the model to better learn the spatial and temporal features necessary for robust video deepfake detection. Frame rate plays a critical role in influencing detection accuracy for video deepfake detection because it determines the temporal resolution of the video, which is essential for identifying inconsistencies across frames. A higher frame rate provides more data points for the model to analyse, allowing it to capture subtle temporal anomalies such as unnatural transitions, mismatched lip movements, or irregular blinking patterns that are often indicative of deepfake videos. The existing research demonstrates that the number of video frames used as input to the CNN-LSTM model has a significant impact on its accuracy. The results from [PERSON] and [PERSON] [32] showed that using 100 frames resulted in a high training accuracy of 99.93% and validation accuracy of 95%, but when the number of frames was reduced to just 10, the training accuracy dropped to 86.75% and test accuracy to 84%. Similar research by [PERSON] [44] deduced that detection accuracy increased as the number of frames used increased, with 100 frames yielding 93.5% accuracy compared to 84.2% for 10 frames. This suggests that the model cannot determine sufficient temporal discrepancies with a smaller frame rate, which is reinforced by [PERSON] et al. [61] who achieved an accuracy of 0.5 across all datasets when using 20 frames. The findings of the research by [PERSON] et al. [35] also show that using too few frames leads to insufficient temporal correlations being captured, while too many frames cause feature redundancy with bias patterns being learned. Their research indicated that 30 frames were ideal, with overfitting and performance loss occurring below that threshold. Likewise, [PERSON] and [PERSON] [40] reported that higher frame rates improved accuracy, as the model was able to learn temporal relationships in a sequential, end-to-end manner, concluding 40 frames to be sufficient for video deepfake. However, using only 10 frames, [PERSON] et al. [47] achieved an accuracy of 84.21%, which increased to 93.58% using 100 frames.