# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion  
The growing interest in video deepfake detection using CNN and LSTM is driven by the increasing sophistication of AI-generated media and the rising concerns over misinformation, identity theft, and digital manipulation. As deepfake videos become more widespread, particularly in areas like politics, entertainment, and cybercrime, the demand for robust detection methods has surged. Traditional video analysis techniques struggle to detect subtle manipulations in deepfakes, making CNN and LSTM highly appealing due to their capabilities to handle complex patterns in image and video data. Researchers are therefore focused on developing and refining deep learning frameworks to enhance the accuracy and efficiency of deepfake detection. The studies reviewed investigate the application of CNN and LSTM to detect deepfakes, showcasing their potential to improve detection accuracy by analysing both spatial and temporal features. Many studies propose innovative hybrid models that combine CNNs for frame-based analysis and LSTMs for temporal sequence analysis to identify the inconsistencies present in fake videos. However, a recurring limitation across these studies is the lack of comprehensive evaluations of diverse datasets and real-world environments, which raises concerns about the generalisability of the proposed solutions. While many papers introduce promising detection methods, they often focus on model design without providing sufficient empirical performance benchmarks, particularly regarding their ability to handle adversarial attacks or cross-dataset generalisation. Additionally, challenges such as computational efficiency, scalability, and the need for real-time detection in high-stakes environments are underexplored. Privacy concerns, as well as the ethical implications of training models on manipulated content, further complicate the development of deepfake detection systems. While the potential of CNNs and LSTMs in video deepfake detection is clear, the absence of extensive evaluations, real-world deployment, and discussion on practical limitations suggests that further research is required to fully address the challenges identified in the current body of work. This section presents a comprehensive and detailed analysis to answer the research questions by integrating insights and contributions from various publications. **RQ1: What is the current landscape of state-of-the-art video deepfake detection studies that utilise CNN with LSTM? **  
The utilisation of CNN in conjunction with LSTM has significantly advanced the field of deepfake detection, pioneered by [PERSON] and [PERSON] [40]. Their model employed an InceptionV3 CNN, removing the fully connected layer at the top of the network whilst using the feature vectors as their LSTM input. The LSTM unit is given a 0.5 chance of dropout to recursively process the frame sequences in a meaningful manner, and the model is trained end-to-end without the need for loss functions. As manipulations can occur at any point in a deepfake model, a continuous subsequence of fixed frame lengths was used as input with classification results achieving 97% accuracy on video feeds less than 2 s. Conversely, the Xception CNN-LSTM model, due to its application of depthwise separable convolutions, has been observed to surpass the capabilities of InceptionV3 in handling spatial information [72]. Additionally, integrating bidirectional LSTMs has contributed substantially to enhancing temporal information modelling capabilities, as observed in several studies [2,14,55]. Investigating the state-of-the-art video deepfake detection studies reveals that these studies can be categorised into several classifications. The first category is the studies that use bi-LSTM to analyse the temporal dynamics of video frames in video deepfake detection [2].