# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
\begin{table}
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**Author** & **Challenge** & **Description of the Challenge** \\ \hline [PERSON] et al. [25] & Discarding frames & This model uses the first 150 sequential frames, which could discard detection of a deepfake in the later portion of the clip. \\ \hline [PERSON], [PERSON], and [PERSON] [62] & Sequence processing & The biggest identified challenge was designing a classifier capable of recursively processing the sequence in a relevant manner. \\ \hline [PERSON] et al. [63] & Computationally expensive & There is a trade-off between the frame rate input into the model and the computational power used. \\ \hline [PERSON] [64] & Training on small datasets & As the model was trained using a small portion of the DFDC dataset, its accuracy decreased when merged with the FF++ dataset. \\ \hline [PERSON] and [PERSON] [65] & Pre-processing to handle sequential data & The model requires extra pre-processing to extract feature vectors from Xception for every fifth frame of the video. \\ \hline [PERSON] et al. [66] & Limited to one dataset & The model trains and tests on the same FF++ dataset. \\ \hline [PERSON] and [PERSON] [67] & Generalisation & Training on specific manipulation techniques hinders the model’s ability to generalise against unseen deepfake attacks, limiting its effectiveness when various manipulation methods are employed. \\ \hline [PERSON] et al. [68] & Performance plateau despite frame increase & At a 60-frame input, performance plateaus where 80 and 100 frames retrieve the same accuracy. \\ \hline [PERSON] [69] & Limited diversity & The model might not be trained on a diverse set of eyes, potentially leading to reduced performance or bias when faced with a broader dataset of eyes. \\ \hline [PERSON] et al. [70] & Lack of genuine imagery & Excluding real imagery from the input, opting to only use the deepfake limits the model’s ability to detect deepfakes in clips in which real and fake content coexist. \\ \hline [PERSON] et al. [71] & Discarding frames & This model uses the first 150 sequential frames, which could discard detection of a deepfake in the later portion of the clip. \\ \hline \end{tabular}
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Table 8: _Cont._ \begin{table}
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**Challenge** & **Suggested Solution** \\ \hline Performance trade-off & To mitigate computational requirements, the LSTM architecture can be leveraged to store essential features and temporal patterns, rather than processing and saving entire video frames on a physical server. This approach significantly reduces the memory overhead, as only relevant and condensed information is retained, instead of storing full-resolution video data for every frame. By focusing on capturing temporal dynamics, LSTM layers can efficiently encode the relationships between video frames. This allows the model to understand changes over time without the need for extensive storage or processing resources. For instance, instead of holding redundant pixel data, the model retains the most meaningful information. \\ \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline Frame Rate Limitation & Analysing the entire video context in a computationally efficient manner, rather than sampling individual frames, offers a more holistic approach to deepfake detection. By considering the full video sequence, the model can better capture temporal inconsistencies and subtle manipulations that may be missed when only isolated frames are examined. This approach ensures that the detection system leverages both spatial and temporal information, providing a more robust understanding of how visual elements evolve over time, which is critical for detecting sophisticated deepfakes. \\ \hline \end{tabular}
\end{table} 
Table 9: Summary of the challenges and their suggested solutions.