# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 4 Analysis of the Results  
The inclusion and exclusion criteria were applied to the collected publications in three phases, according to the PRISMA 2020 statement [28]. In the first phase, a total of 674 articles were identified from six different databases: Google Scholar (210), IEEE Explore (38), PubMed (2), Elsevier ScienceDirect (90), ACM Digital Library (195), and SpringerLink (139). Then, in phase 2, the collected articles were screened based on the research questions where the articles that did not align with the research questions, were out of scope, or did not meet the inclusion criteria were excluded. This resulted in excluding 601 articles and moving forward with 73 articles. In phase 3, 28 duplicate articles were identified and removed from the 73 articles, leaving 45 articles that were included in this review. The flow diagram of the PRISMA process and the number of articles at each stage is shown in Figure 5.  
By applying the criteria to the online databases, the number of articles available was reduced to 45 throughout the three phases. The results within Table 1 show that Google Scholar provided the most relevant publications, while PubMed had the least publications related to using CNN with LSTM for video deepfake detection. Figure 6 illustrates the number of publications per year. The graph suggests that the research focus on CNN and LSTM technologies for video deepfake detection particularly emerged from 2020 onward. The large spike in 2020 indicates a growing interest in this field, possibly due to advancements in technology and the rise in deepfake-related incidents. The 2022 peak aligns with the easing of the COVID-19 pandemic, suggesting that research activities resumed or increased following any pandemic-related delays. The consistency between 2022 and 2023 shows sustained interest in the area, with researchers continuing to explore these technologies for video deepfake detection. Figure 5: The outcome of the three-phase selection process. The final publication list that was involved in this review is shown in Table 2, which assigns a publication ID to each publication, and includes the authors, year of publication, and publication type. \begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Author** & **Year** & **Publication Type** \\ \hline
1 & [PERSON] et al. [31] & 2024 & Journal \\ \hline
2 & [PERSON] and [PERSON] [32] & 2021 & Conference proceedings \\ \hline
3 & [PERSON] and [PERSON] [33] & 2024 & Journal \\ \hline
4 & [PERSON] and [PERSON] [34] & 2020 & Conference proceedings \\ \hline
5 & [PERSON] et al. [35] & 2020 & Conference proceedings \\ \hline
6 & [PERSON], [PERSON], and [PERSON] [36] & 2022 & Journal \\ \hline
7 & [PERSON] et al. [37] & 2023 & Conference proceedings \\ \hline
8 & [PERSON] et al. [2] & 2020 & Journal \\ \hline
9 & [PERSON], [PERSON], and [PERSON] [38] & 2023 & Conference proceedings \\ \hline
10 & [PERSON] et al. [39] & 2023 & Conference proceedings \\ \hline
11 & [PERSON] and [PERSON] [40] & 2018 & Conference proceedings \\ \hline
12 & [PERSON] et al. [41] & 2020 & Journal \\ \hline
13 & [PERSON] et al. [42] & 2021 & Journal \\ \hline
14 & [PERSON] et al. [43] & 2022 & Conference proceedings \\ \hline \end{tabular}
\end{table} 
Table 2: Retrieved publications. \begin{table}
\begin{tabular}{l c c c} \hline
**Database** & **Phase 1** & **Phase 2** & **Phase 3** \\ \hline IEEE Xplore & 38 & 20 & 13 \\ Google Scholar & 210 & 18 & 10 \\ ACM Digital Library & 195 & 12 & 7 \\ SpringerLink & 139 & 9 & 6 \\ PubMed & 2 & 1 & 1 \\ Elsevier ScienceDirect & 90 & 13 & 8 \\ Total & 674 & 73 & 45 \\ \hline \end{tabular}
\end{table} 
Table 1: Selected publications from online databases. Figure 6: Number of publications per year.