# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 3 Research Methodology  
This systematic literature review (SLR) aims to define, analyse, and interpret all available research relevant to utilising CNN and LSTM for video deepfake detection. While video deepfake detection has become an area of growing interest, the complexity and evolving nature of deepfake technology necessitates an in-depth examination of current detection techniques. This SLR investigates the current landscape of video deepfake detection methods that leverage the hybrid model of CNN combined with LSTM. This review identifies the most common feature extraction techniques utilised in these models and evaluates the datasets frequently used for training and testing. By systematically reviewing state-of-the-art approaches, this paper aims to highlight key trends and offer insights into optimising CNN-LSTM models for enhanced detection accuracy.  
To ensure transparency, reproducibility, and scientific rigour, this review follows the Preferred Reporting Items for Systematic Review (PRISMA) 2020 protocol developed by [PERSON] et al. [28]. PRISMA was first introduced in 2009 and is widely used across various disciplines. We selected PRISMA due to its comprehensive nature and its potential to promote consistency in systematic reviews. The PRISMA protocol outlines five key stages, as shown in Figure 4, for conducting an SLR, which were applied in this paper. The first stage involves formulating research questions that guide the review process. Following this, inclusion and exclusion criteria are established to ensure that the selected studies are relevant and aligned with the research objectives. In the third stage, relevant research databases are identified and searches are conducted to retrieve pertinent literature. The fourth stage focuses on analysing the findings from the reviewed studies, and in the fifth stage, the outcomes and the results are discussed.  
Figure 3: A bidirectional LSTM block.  
Figure 2: A standard LSTM block.  
Based on this strategy, 45 (out of 674) papers were selected, discussed, and criticised, leading to a section on open issues and further research. This allows researchers to explore the topic in depth to develop CNN-LSTM detection techniques and tools based on advanced findings.  
Although this paper aims to provide detailed coverage of the topic, there may be limitations through constraining the scope such as the risk of overlooking important field contributions resulting from research outside of the inclusion and exclusion criteria. For example, limiting the scope to papers that were published in English biases the findings from non-English-speaking authors, despite their research following the same methodologies. Therefore, the source selection method may restrain the research availability, and the PRISMA statement is necessary. Additionally, publication bias could occur as the search strategy only identifies published research within known databases [29]. Although this research is regarded higher, studies show that results with positive findings are more likely to be published in high-impact journals [30], and therefore valid research is potentially excluded. However, the benefits of the peer reviewing of these journals ensure that the research is integral. Further limitations exist in the form of subjectivity, as prior knowledge on the larger topic may impact assumptions on what is regarded as'sufficient enough' research to include within the SLR.