# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
Although feature extraction learns more from detailed imagery, testing the parameters and reducing the input size by using smaller images and a lower frame rate helps equilibrate the trade-off when access to larger computational power is not available  
The computational trade-offs in CNN-LSTM models for deepfake detection are critical, particularly for real-time applications. These models combine spatial and temporal analysis, requiring significant computational resources, which can hinder their ability to function efficiently in real-time scenarios. High computational costs result from processing large video datasets, the complexity of CNN layers for feature extraction, and the LSTM's sequential frame analysis. To enable real-time detection, there is often a need to simplify the model, reduce the input data, or limit the depth of Neural Networks. However, these optimisations can negatively impact the accuracy of deepfake detection, as reducing model complexity typically leads to less effective feature extraction and pattern recognition. Balancing accuracy with speed is thus a key challenge, especially when considering deployment in environments with limited computational resources, such as mobile devices or real-time video streaming platforms. Furthermore, real-time deepfake detection leveraging CNN-LSTM architectures is often constrained by the availability of hardware acceleration, such as GPUs, which can significantly influence detection viability. Hardware acceleration enhances real-time speed whilst reducing latency and power consumption, which is ideal for real-time detection [61]. Where this is not available, optimising models to function on less powerful hardware using lightweight CNNs such as MobileNet [62] or Efficient [63] is effective. The computational strain of CNNs can be reduced using model compression through techniques like model pruning or quantisation, which lower the precision of weights and reduce memory usage. Compressing the CNN-LSTM model refers to reducing the size of the model without sacrificing performance. Pruning the model uses deep learning to remove less important weights and connections from the network to reduce the number of operations [64]. Quantisation involves reducing the precision of the weights to reduce computational power and reduce memory between nodes, therefore also enhancing processing speeds [64]. However, aggressive compression can lead to a reduction in the models ability to detect fine-grain details. In time-sensitive scenarios, such as real-time live streams, these trade-offs can make the distinction between effective and ineffective detection systems. Another significant challenge relates to the limitations of frame selection in deepfake detection models. One of the main issues with frame selection is the inherent variability in deepfake videos. These videos often employ sophisticated techniques to manipulate specific frames, making certain frames more susceptible to detection than others. Consequently, if the selected frames do not adequately represent the video as a whole, the model may struggle to identify subtle anomalies that indicate manipulation. For instance, a deepfake might appear convincing in a few selected frames but exhibit detectable artefacts in others, such as unnatural facial movements or inconsistencies in lighting and shadows. If the model only trains on frames that do not capture these discrepancies, it may fail to generalise to new instances of deepfakes. [PERSON] and [PERSON] [32] attempted to reduce computational costs by limiting their CNN-LSTM model to the first 100 sequential frames, which can result in missed deepfake instances occurring later in the video. Similar frame rate restrictions were observed in research by [PERSON] et al. [70], where models trained only on deepfake frames struggled to generalise to real-world videos containing a mix of authentic and manipulated content.