# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
For instance, models might learn to recognise deepfakes based on dataset-specific artefacts, such as particular lighting conditions or facial attributes, rather than general patterns that could apply across all types of deepfakes. Additionally, the scarcity of large, diverse, and standardised benchmark datasets limits the robustness of these models. Addressing this challenge requires expanding the diversity of training datasets and employing techniques like transfer learning and domain adaptation. These approaches could improve the model's ability to adapt to new data without requiring extensive retraining on each new dataset. Furthermore, incorporating multimodal approaches, which analyse both visual and audio cues, could enhance the generalisation of CNN-LSTM models, making them more robust in real-world applications where deepfake content varies widely. **RQ4: What are the factors that have the strongest influence on detection accuracy for video deepfake detection when implementing CNN with LSTM? **  
After reviewing the selected research papers, the strongest factors influencing detection accuracy for video deepfake detection when implementing CNN with LSTM include the LSTM architecture, training rate, frame quality, and the presence of facial landmarks. LSTM architecture has a significant influence on detection accuracy for video deepfake detection because it excels in capturing temporal dependencies in sequential data. Since deepfakes often involve dynamic, frame-by-frame manipulations of videos, LSTM models are particularly well suited for detecting inconsistencies that occur over time, such as unnatural movements or frame transitions. Unlike traditional convolutional networks that focus on spatial features within individual frames, LSTMs analyse the video as a sequence, allowing the model to learn the patterns of both natural and manipulated content over time. By doing so, LSTM networks can detect subtle anomalies in facial expressions, lip movements, or eye blinks that may be temporally inconsistent with natural human behaviour, leading to higher detection accuracy. The integration of bidirectional LSTM with CNN has a significant influence on improving the detection accuracy of video deepfake detection models. The use of bidirectional LSTM layers, which extract temporal data through a backward pass, resulted in higher detection accuracy compared to unidirectional LSTM layers [2]. For example, on the FF++ and Celeb-DF datasets, the bidirectional LSTM approach achieved detection accuracy values of 99.7% and 97%, respectively, outperforming the unidirectional LSTM which only attained 92% and 84%. Similar findings by [PERSON] et al. [39] stated that utilising bidirectional LSTM layers to represent the sequential nature of video data mitigates overfitting and leads to a higher prediction accuracy than traditional LSTM approaches. Although their model did not outperform all state-of-the-art methods, it achieved robust results with a significantly smaller number of trainable parameters, at around 59 million whilst incorporating emotional features. Furthermore, the findings of [PERSON], [PERSON], and [PERSON] [55] leverage the ability of dense connections to propagate negative scores during feature extraction to extract a more dense and informative set of visual characteristics, which, when coupled with bi-LSTM for temporal correlation, results in a substantial increase in classification accuracy compared to the VGG16-based LSTM approach. The DenseNet121 and GoogleNet models also benefited from the integration of bi-LSTM, achieving detection accuracies of 98.11% and 97.91%, respectively. Therefore, the combination of CNN and Bi-LSTM can increase detection accuracy for video deepfake detection despite its high computational cost. Training rate, often referred to as the learning rate, also has a great influence on detection accuracy for video deepfake detection because it directly affects the model's ability to learn patterns effectively during the training process.