# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
Overall, the evidence suggests that the number of frames used as input impacts the detection accuracy as the model can better detect temporal variances for classification, with the ideal range of frames for detection being between 30 and 100, depending on the available resources. Another factor that impacted detection accuracy was the quality of the frames taken from the video, as high-quality frames provide clear and detailed visual data, which is critical for identifying subtle manipulations. Deepfakes often involve minor distortions in facial features, skin texture, or lighting conditions that may be imperceptible in low-resolution or poor-quality frames. High-quality frames preserve the fine details necessary for detection models, especially CNNs, to capture these anomalies and make accurate distinctions between real and fake content. [PERSON] et al. [14] discovered their model performed 10% better on high-quality videos from the DFDC dataset compared to low-quality videos and also achieved higher AUC scores for medium compression levels versus high compression. They attribute these improvements to incorporating minor training optimisations, including assigning different update rates per network layer and using dropout, as well as their proposed loss function. Similarly, [PERSON] et al. [50]observed that their detection methods were able to correctly classify videos with lower deepfake quality but struggled with higher-quality manipulations. [PERSON], [PERSON], and [PERSON] [38] also reported that their model achieved better accuracy and precision when operating on cropped and resized face frames at 512 \(\times\) 512 resolution, rather than full video frames. Collectively, these findings underscore how CNN-LSTM models require input pre-processing to reach their optimal detection accuracy. Additionally, they are robust to higher-quality images for detection but require further work to tune the models to have a stronger performance on lower-quality deepfakes. The findings by [PERSON] et al. [39] suggest applying Contrast Limited Adaptive Histogram Equalization (CLAHE) during pre-processing to improve the contrast in images, enhancing the definitions of edges in each region, meaning the CNN can detect facial features more effectively in the feature extraction stage. Facial landmarks also have a strong influence on detection accuracy for video deepfake detection because they serve as key reference points for identifying natural facial movements and expressions. Deepfake algorithms often struggle to perfectly replicate the subtle dynamics of facial features, such as the alignment of eyes, nose, mouth, and jawline during speech or expression changes. By focusing on these landmarks, detection models can identify irregularities in how these points shift or interact over time, exposing unnatural distortions that are indicative of manipulation. The precise tracking of facial landmarks allows the model to detect even minor inconsistencies in facial behaviour, significantly boosting the accuracy of deepfake detection systems. [PERSON] [41] determines that detection accuracy increases for every facial landmark introduced, only performing with 26.8% accuracy using eye blinking when using an MTCNN with LSTM but 94% accuracy when the mouth movement is included. Other studies reinforce these conclusions [65], establishing that training individual facial regions and applying late fusion does not yield higher accuracy in comparison to training on the full face, although detection of individual facial features on the FF++ dataset resulted in 0.98 AUC scores. Therefore, this detection method is not robust for entire face synthesis or identity swap deepfake methods but could be applied to detecting attribute manipulation. Comparably, other research by [PERSON] et al. [66] deduced the mouth and eyes to be the most important features in detection, concluding that performance increases significantly when a particular feature of the facial data is strengthened through the removal of other features.