# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
As deepfake technology advances, these hybrid content videos may become more prevalent, and models that do not account for them risk incomplete or inaccurate detection. Other researchers, such as [PERSON] and [PERSON] [45], restricted their models to 100 fps input to limit computational expenses, but doing so can limit the model's ability to detect temporal inconsistencies, reducing overall robustness. A related issue is the reduction in frame rates during the pre-processing stage to save computational resources. Some studies employed techniques that sample every fifth frame, which, while efficient, can negatively impact the ability to capture critical temporal dynamics necessary for accurate detection [65]. A reduction in temporal resolution may cause the model to miss subtle manipulations or patterns needed for deepfake detection. This challenge becomes more apparent when dealing with deepfakes that feature fast transitions or nuanced alterations, suggesting that while frame sampling is useful for reducing computational load, it could compromise detection accuracy in more complex scenarios. [PERSON] and [PERSON] [32] reduced the frame rate input into the CNN-LSTM model, limiting the model to utilise the first 100 sequential frames. Similarly, researchers [25; 71; 57] employed models that only considered the first 150 sequential frames, therefore risking the possibility of not detecting deepfake instances in the remaining parts of the video. Research from [PERSON] et al. [70] suffered from a similar limitation, where the model input consisted solely of the deepfake frames of the video. This approach is not representative of all real-world scenarios, where videos can contain a mix of both authentic and manipulated content. As deepfake technology advances, more videos may feature a hybrid of real and deepfake elements to mislead the viewer. Furthermore, [PERSON] and [PERSON] [45] restricted their model to accept a maximum frame rate input of 100 fps to limit computational expenses. Although this is still considered a high frame rate, placing limitations on the frame rate can limit the model's ability to capture temporal inconsistencies. This is reinforced by other findings by [PERSON] et al. [61], which only achieved an accuracy of 50% when using 20 fps as input. This continues in the findings of [PERSON] and [PERSON] [65] where the frame rates are reduced within the pre-processing stage by sampling every fifth frame. Although this reduces computational expenses, skipping intermediary frames can affect the model's ability to accurately capture temporal dynamics. A reduction in the temporal resolution of the frames from lower frequency sampling could cause fine details required for detecting patterns or anomalies to be missed. Therefore, the robustness of the model could be reduced when applied to deepfake videos with fast or subtle manipulations. An investigation into the optimal frame rate input for CNN-LSTM architecture can support future researchers by allowing their models to be less computationally expensive and finetune the pre-processing stage; however, as models operate on different hardware and frames are of varying quality, the optimal baseline may vary. The ability to handle hybrid content is vital for the development of robust and reliable video deepfake detection systems. Addressing this limitation by analysing the entire video context in a computationally efficient manner, rather than a small sample of individual frames, could lead to more effective detection systems. [PERSON] et al. [43] introduce finding the correlation coefficient between subsequent frames as a solution to the computational expense of a high frame rate, where preceding frames are discarded from being fed into the network if they have a threshold difference greater than 0.005. This keeps input small and selects essential frames for the highest detection accuracy during the pre-processing stage. The results of [PERSON] et al. [68] show the CNN-LSTM model's performance plateaued at a 60 fps input.