# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 6 Open Issues and Future Research Directions
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**Challenge** & **Suggested Solution** \\ \hline \multirow{8}{*}{Restricted Frame Rate Input} & Utilising motion vectors and information masks from the H.264 video codec significantly enhances the efficiency of detecting temporal inconsistencies in video content. Instead of analysing every individual frame, this approach leverages the motion vectors, which represent the direction and magnitude of pixel movement between frames. By focusing on these vectors, the system can identify areas of significant change without the computational overhead associated with full RGB analysis. Information masks further refine this process by highlighting critical regions within the video, enabling targeted scrutiny of potentially manipulated areas. This dual strategy not only reduces computational costs but also preserves detection effectiveness, allowing for quicker analysis while maintaining accuracy, especially in scenarios involving high frame rates or large datasets. \\ \hline \multirow{8}{*}{Video Compression} & Pre-processing techniques that combine frequency enhancements with colour domain transformations significantly enhance the model’s resilience to compression artefacts. By fusing these enhancements, the model can better capture essential features that may be obscured in highly compressed videos. Additionally, using architecture-specific loss functions optimises training by emphasising relevant characteristics for deepfake detection, allowing the model to focus on crucial elements even when subtle details are compromised. This holistic approach ensures that the model maintains accuracy and effectiveness in identifying deepfakes, even in challenging scenarios where traditional methods may falter due to the loss of critical information. \\ \hline \multirow{8}{*}{Facial Occlusions} & Adapting pre-processing and feature extraction techniques to focus on key landmarks associated with facial rotations enhances the model’s ability to recognise faces under various orientations. This involves training the model to identify critical facial features, such as the eyes, nose, and mouth, despite changes in position. Additionally, designing the CNN-LSTM architecture to effectively track facial dynamics over time allows for the capturing of essential temporal information, ensuring that the model maintains a coherent understanding of the face throughout the video sequence. This dual approach significantly boosts the model’s robustness against facial occlusions, improving its accuracy in detecting deepfakes even when facial information is partially obscured. \\ \hline \multirow{8}{*}{Lighting Conditions} & Training on datasets that encompass a diverse array of facial characteristics including variations in facial hair, skin tones, and distinctive features helps to minimise bias in the model. This diversity enables the model to learn a broader range of patterns and characteristics, thereby enhancing its generalisability across different populations. As a result, the model becomes more adept at recognising authentic faces, regardless of variations in appearance. By being exposed to various features, the model improves its effectiveness in detecting unseen deepfake manipulation techniques. This approach not only strengthens the model’s performance on diverse inputs but also enhances its reliability in real-world applications, where facial characteristics can vary significantly. \\ \hline \multirow{8}{*}{Evolving Deepfake Techniques} & Continuously testing and adapting CNN-LSTM models to keep pace with emerging deepfake techniques is crucial for maintaining their relevance and effectiveness. As new manipulation methods, such as the neural textures subset, evolve, models must be updated to recognise and respond to these advanced threats. This ongoing process involves regular evaluation of the model’s performance on new datasets and incorporation of recent findings in deepfake technology. By refining the model through iterative training and testing, it becomes more resilient to previously unseen attacks, ensuring that it can accurately detect deepfakes across a wide range of scenarios. This proactive approach not only bolsters the model’s defence mechanisms but also solidifies its role as a reliable tool in combating deepfake proliferation. \\ \hline \hline \end{tabular}
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