# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 1 Introduction  
Deepfakes are a form of online media depicting digitally synthesised humans. When used maliciously, this technology harms the fundamental human right to privacy, with the freedom of unreasonable constraints on the construction of one's own identity threatened through the often-non-consensual use of personal identities [1]. Therefore, the creation of deepfake detection systems acts as a privacy technology to ensure humans can identify authentic content whilst upholding the integrity of democratic political systems, providing transparency for manipulated audio and visual clips that are created to cause harm and spread misinformation. For example, deepfakes of political leaders urging the public to vote for a specific party or relaying false information about medical practices pose a threat to democracy and well-being [2]. Several Machine Learning (ML) techniques and tools have been developed for deepfake video detection to combat the growing threat of synthetic media manipulation. Common approaches include CNNs and Recurrent Neural Networks (RNNs), which can detect subtle artefacts or inconsistencies in facial movements, lighting, and textures often missed by the human eye [2]. Tools such as FaceForensics++, Deepware Scanner, and Microsoft'sVideo Authenticator leverage these techniques to identify discrepancies between real and manipulated content. Researchers also employ adversarial training, where a Generative Adversarial Network (GAN) creates deepfakes while another model attempts to detect them, improving accuracy over time. These ML-based techniques continuously evolve to counter increasingly sophisticated deepfake technologies, providing crucial defences against misinformation, fraud, and identity theft [3,4]. The combination of CNN and LSTM has proven to be particularly effective for video deepfake detection, offering impressive accuracy levels, sometimes approaching 100% [2]. CNNs shine at extracting spatial features from individual video frames, capturing fine-grained details such as inconsistencies in facial textures, lighting, and image resolution. LSTM networks, on the other hand, analyse temporal patterns, detecting irregularities in how these features evolve over time, which can signal deepfake manipulation. By integrating both spatial and temporal analysis, this hybrid model allows for a comprehensive evaluation of videos, making it difficult for deepfakes to evade detection. This approach not only boosts accuracy but also provides a more robust defence against increasingly sophisticated deepfake algorithms, ensuring that manipulated content is identified more reliably in real-world applications [2,3]. Although the integration of CNN and LSTM for video deepfake detection provides countless opportunities and advantages compared to relevant techniques, there is a lack of research studies that systematically evaluate the effectiveness of this hybrid model and provide the necessary details for the effective implementation of this model. While existing studies demonstrate the potential of this hybrid model, they often focus on isolated implementations and lack a comprehensive review of the techniques, datasets, and performance metrics used across various approaches. There is limited insight into the optimal configurations of CNN-LSTM architectures and the best practices for improving detection accuracy in different contexts, particularly as deepfake generation techniques evolve. Furthermore, the challenges of high computational costs and scalability issues associated with training these models remain underexplored. This paper aims to provide a comprehensive investigation of the current landscape of video deepfake detection techniques that utilise the hybrid model of CNN with LSTM. By systematically reviewing state-of-the-art approaches, this paper identifies the most common feature extraction techniques employed in these tools and examines the datasets frequently used for training and testing.