# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
The overall detection accuracy on real videos was over 90\%. \\ \cline{1-1}  & The proposed model is unable to detect deepfakes from a diverse set of eyes, suggesting a bias in its training data or architecture. \\ \cline{1-1}  & The proposed model is able to detect deepfakes from a diverse set of eyes, suggesting a bias in its training data or architecture. **RQ2: What are the most common feature extraction techniques used in video deepfake detection tools that utilise CNNs with LSTM? **  
Many of the feature extraction techniques can be categorised based on the backbone networks they are built upon, where a pre-trained CNN that has been trained on a large dataset for image classification is employed for sequential data analysis. Subsequently, they are followed by an LSTM layer, as LSTM can sequentially process video frames by comparing frames at \(t\) seconds with the frame \(t-n\) seconds, where n is any number of frames before \(t\) seconds [25]. The most common feature extraction baseline network utilised for video deepfake detection is based on ResNet architecture, which uses depth and width dimensions and residual blocks to enable shortcut'skip' connections. This is when the gradient signal bypasses layers within the network by performing identity mapping and adding their outputs to the outputs of the stacked layers without adding extra parameters or computational complexity [73]. Therefore, when the network is trained via backpropagation, the vanishing gradient problem is mitigated and high-level features learned by the network are preserved. [PERSON] et al. [41] adopt a transfer learning approach, using a pre-trained ResNet CNN as the foundation for feature extraction to identify the spatial locations of features within the frames. A dedicated feature extractor network is derived from identifying a layer within the CNN to dedicate for feature extraction and removing subsequent layers. Integrating this with LSTM, each video frame has its time and patterns memorised and the recurrent nature of the cell computes the temporal dependencies between the frames. ResNet architecture has several forms, notably, 18-layer and 34-layer plain networks or 50-layer bottleneck models. The bottleneck models are considered more accurate due to their increased depth without the degradation problem [73]. While [PERSON] et al. 's [46] adoption of the ResNet18 architecture with Gaussian blur is creditable in its performance and ability to remove high-frequency noise to recognise more important characteristics, the choice of a shallower ResNet model may limit its capacity to capture intricate spatial features. Integrating Recycle-GAN to merge spatial and temporal data signifies a step towards addressing this limitation, enhancing the model's ability to learn as it feeds its results back through the network to update its weights and parameters [74], therefore potentially improving detection accuracy. Combining the Recycle-GAN with a deeper ResNet model could optimise its performance. In ResNet-50 architecture, each two-layer block in the 34-layer model is replaced with a three-layer bottleneck block, where the three layers consist of 1 \(\times\) 1, 3 \(\times\) 3, and 1 \(\times\) 1 convolution, which are responsible for reducing and restoring input and output dimensions. [PERSON] [69] adopts this architecture to classify eye states as open or closed and then sets an eye aspect ratio to detect the eye-blinking rate within the frames followed by LSTM for sequence learning. ResNeXt architecture is a successor of Resnet that solved the limitations of depth scalability issues whilst maintaining computational efficiency. It introduces a new dimension named cardinality--which refers to the number of parallel paths within a residual block--to improve classification accuracy by leveraging grouped convolutions to capture a more diverse set of features [75]. The widespread use of ResNeXt-50 (32 \(\times\) 4d) with