# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 7 Conclusions  
The integration of CNN with LSTM networks has emerged as a promising strategy for enhancing video deepfake detection, demonstrating remarkable accuracy levels that can approach 100%. This hybrid approach capitalises on the unique strengths of CNNs in extracting spatial features from individual video frames, allowing for detailed analysis of visual elements such as facial textures, lighting inconsistencies, and resolution anomalies. At the same time, LSTMs shine at analysing temporal patterns, providing insights into how these visual features evolve and enabling the detection of irregularities that may indicate manipulation. By effectively combining spatial and temporal analyses, this hybrid model significantly improves the evaluation of videos, making it increasingly difficult for deepfakes to evade detection. While the potential of CNN-LSTM models is evident, the existing research lacks systematic evaluations that comprehensively assess their effectiveness, optimal configurations, and best practices across various contexts. This paper presents a comprehensive systematic review that assesses state-of-the-art video deepfake detection tools, focusing on the key factors influencing detection performance in this rapidly evolving field. This review emphasises the critical importance of understanding both the capabilities and limitations of current detection systems as deepfake technology advances. Following a rigorous search strategy, a total of 45 articles were selected from 674 publications across multiple reputable databases. The selected articles were examined to evaluate recent advancements in video deepfake detection methodologies using CNN-LSTM. This paper identifies the most common feature extraction techniques employed in these tools and examines the datasets frequently used for training and testing. This paper also evaluates the performance of these models on different datasets to highlight their strengths and limitations. Additionally, this paper compares CNN-LSTM models with alternative approaches that do not employ LSTM to assess their effectiveness in deepfake detection. This paper also explores the challenges associated with implementing these tools and proposes potential solutions to overcome these barriers. Lastly, this paper presents open issues and research directions that are critical including the need for real-time detection methods, strategies for improving model generalisation across diverse datasets, the establishment of standardised performance metrics, and techniques for effectively detecting low-quality and compressed deepfakes. Addressing these issues is essential to enhance the robustness and applicability of deepfake detection systems in real-world scenarios.  
Conceptualisation, H.F.A. and S.T.; methodology, H.F.A.; software, S.T.; validation, S.T., H.F.A. and H.S.L.; formal analysis, S.T and H.F.A.; investigation, S.T.; resources, S.T. and H.S.L.; data curation, S.T.; writing--original draft preparation, S.T and H.F.A.; writing--review and editing, H.S.L. and H.F.A.; visualisation, S.T.; supervision, H.F.A. and H.S.L.; project administration, H.F.A. and H.S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.