# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
The results concluded emotional analysis is a robust method of deepfake detection, although facial representations of emotion can differ amongst age, gender, and culture, therefore making a non-biased baseline hard to establish on the limited datasets available. Prediction error rate and its effect on the effectiveness of CNN--LSTM models was also another category discussed in the literature. When dealing with compressed video feed, [PERSON] and [PERSON] [34] use the prediction rate error between frames, averaging 94.29% detection accuracy on lossless videos in the FaceForensics++ dataset. This involves calculating the difference between the current macroblock and motion-compensated macroblock to look for residual errors in predicting the succeeding frame to determine whether a deepfake face has been inserted into the video and altered the sequence structure. The model uses a 2D convolutional layer and a max pooling layer to have time (\(t\)) distributed output, which is then input into an RNN with LSTM cells, with every LSTM cell considered input with its previous LSTM cell state at \(t-1\). Later research [61] outperforms [PERSON] [PERSON] [34], reaching 97.25% accuracy on compressed videos in the same dataset using ResNeXt-50 architecture with an LSTM layer. Another category discussed in the literature studies was CNN-LSTM hybrid models. The use of CNN-LSTM hybrid models across various research papers is a prevalent approach to deepfake detection. [PERSON] et al. [31] propose a model that combines CNN with LSTM, optimised using PSO, whereas [PERSON] and [PERSON] [32] use a ResNeXt-50 backbone alongside LSTM to bridge the gap between training and test accuracy. Both papers highlight the importance of optimising CNN-LSTM architectures, yet they differ in the optimisation techniques used (PSO vs. traditional gradient descent) and the datasets on which they are applied. Similarly, the work by [PERSON] and [PERSON] [40] also employs InceptionV3 CNN with LSTM for deepfake detection, achieving 97% accuracy with limited frames (40-80 frames). This is comparable to [PERSON] et al. [41], who propose CNN-LSTM for efficient feature extraction without memory expense, but their model detects only key facial landmarks rather than additional features, such as noise or blur, which limits its robustness. In both cases, the emphasis on balancing spatial and temporal features is evident, but the strategies for handling data limitations diverge. [PERSON] and [PERSON] [33] and [PERSON] et al. [48] also introduce hybrid CNN-LSTM models, achieving high accuracy on benchmark datasets, such as DFDC and Ciplab. However, [PERSON] and [PERSON] [33] focus on general deepfake detection, while [PERSON] et al. [48] specifically address face-swapping manipulations, which illustrates a common theme: while many papers rely on similar CNN-LSTM architectures, their focus areas and application domains differ significantly. [PERSON] and [PERSON] [34], for instance, target video-based manipulations using prediction errors between frames but struggle with compressed video data, which limits its general applicability. An emerging trend is the integration of attention mechanisms to improve detection performance. For example, [PERSON], [PERSON], and [PERSON] [36] employ a spatiotemporal attention mechanism within a ConvLSTM framework, and [PERSON], [PERSON], and [PERSON] [38] introduce a multi-head attention layer for stronger video classification performance. These attention mechanisms focus on enhancing critical feature selection, further refining CNN-LSTM models for greater generalisability and efficiency. However, as noted by [PERSON] et al. [36], increasing model complexity can lead to overfitting, a concern repeated by [PERSON] et al. [38], whose lightweight model overfits after increasing the number of LSTM layer units beyond 128. The last category of studies investigated the generalisability issue in CNN-LSTM models. A key limitation across many models is their generalisability to new or unseen datasets. For instance, [PERSON] et al.