# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
Increasing the frame rate to 80 fps and 100 fps did not yield any significant improvement in the accuracy measures, which remained consistent at 97% across the two datasets, with a variance of only 0.3%. This implies that beyond a threshold of 60 frames per second, additional temporal information does not contribute to improving the model's performance. The performance plateau observed suggests that the LSTM cells do not leverage long-range temporal information or the CNN layers in the CNN-LSTM architecture may not be fully effective in extracting the necessary spatial and temporal features required for accurate detection. Therefore, to ensure robust models are created, authors should test their models under a range of conditions to identify which architectural features or parameters hinder performance. However, when finetuning the CNN and LSTM bias and weight parameters using the PSO algorithm, an increased number of iterations did not lead to an increase in accuracy [31] An alternative solution is to integrate existing video codec information into the process instead of selecting frames. Information masks from the H.264 video codec are used to detect temporal inconsistencies using the preceding frames to map changes in the motion vector's spatial location [89]. Compression of video data further complicates deepfake detection, as CNN-LSTM models are sensitive to compression artefacts. Video compression can have a significant impact on the performance of CNN-LSTM-based video deepfake detection models. [PERSON] and [PERSON] [34] found that their CNN-LSTM model demonstrated poor performance on strongly compressed data, indicating limitations in detecting deepfakes in highly compressed videos. However, the classification accuracy was better on lossless and level 23 and 40 compressed videos with LSTM employed than using a CNN alone, demonstrating that exploiting sequence correlations can help in distinguishing deepfake videos. Similarly, [PERSON] et al. [2] deduced that video compression adversely affects detection accuracy, particularly for the FaceSwap subset of the FaceForensics++ dataset. This model outperformed the Xception network on compressed MPEG and JPEG videos, but Xception was significantly better at detecting deepfakes in the FaceSwap subset, which was more heavily impacted by compression. Regarding audio channels, research [2] also found that increased audio compression made it harder to detect real from spoofed audio, emphasising the need for deepfake detection models to be robust to both video and audio compression. Also, the results of [PERSON] et al. [14] demonstrated that a CNN-bi-LSTM model with a newly proposed loss function could achieve better frame-level detection performance for videos with medium compression rates, increasing the AUC score from 0.92 to 0.99. Additionally, [PERSON] et al. 's [64] ResNeXt-50-based model achieved 97.5% accuracy on compressed videos (c = 23) when training for 13 epochs, showing that intricate feature extraction and early stopping during training can overcome the challenges posed by video compression. Overall, the findings suggest that incorporating techniques to handle compression, such as pre-processing data with a fusion of frequency enhancements and colour domains alongside architecture-specific loss functions, can enhance the robustness of CNN-LSTM-based video deepfake detection models. Facial occlusions remain a challenge for video deepfake detection models, requiring a larger frame rate to compensate for the loss of facial information [41]. The findings concluded that facial rotations limit the model's ability to classify without a full face showing, with testing accuracy only reaching 40.2%. This is reinforced by researchers [48] who observed that when the video subject looks away, detection can be compromised. To mitigate this challenge, the detection model should adapt its pre-processing and feature extraction techniques to detect and learn the key landmarks for facial rotations ranging from 45 to 90 degrees.