# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
Multiple studies [59; 60] demonstrated that their CNN-LSTM models had the lowest performance metrics on neural texture deepfakes, therefore indicating that these models have a stronger performance on GAN-generated deepfakes. Adversarial attacks pose a unique challenge to CNN-LSTM models due to their ability to target both spatial features, handled by CNNs, and temporal coherence, processed by LSTMs. Adversarial examples can be crafted to disrupt the temporal patterns between video frames or introduce pixel-level distortions that evade detection by the spatial filters of CNNs. Adversarial training of CNN-LSTM models using high-quality, imperceptible adversarial deepfakes will strengthen the robustness of the detection system as it learns to be vigorous against adversarial attacks. By intentionally adding Gaussian noise to deepfakes to misclassify them, the model continuously optimises as it learns to accurately reconstruct and classify the images. Table 8 summarises the challenges encountered when implementing the CNN-LSTM models in video deepfake detection in the selected publications. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Challenge** & **Description of the Challenge** \\ \hline [PERSON] et al. [31] & Performance plateau due to parameter tuning & Fine-tuning the parameters through iteration using the PSO algorithm did not always lead to an increase in accuracy. \\ \hline [PERSON] and [PERSON] [32] & Discarding frames & This model only uses the first 100 sequential frames, which could discard detecting a deepfake in the later portion of the clip. \\ \hline [PERSON] and [PERSON] [33] & Computationally expensive & This model has a trade-off between accuracy and computational efficiency \\ \hline [PERSON] and [PERSON] [34] & Low performance metrics on highly compressed videos & The CNN-LSTM model demonstrated poor performance on strongly compressed data, indicating limitations in detecting deepfakes in highly compressed videos. \\ \hline [PERSON] et al. [35] & Default assumption that the content is not deepfake & The model operates under the default assumption that the content is not deepfake, which could then potentially overlook key features of a deepfake. \\ \hline [PERSON], [PERSON], and [PERSON] [36] & Computationally expensive and time consuming & The model has an increased runtime compared to other algorithms due to the non-parallelisable convolutional operations of ConvLSTM, resulting in a worst-case time–space complexity of O(\(n^{2}\)). \\ \hline [PERSON] et al. [37] & Computationally expensive & This model requires a high number of frames to be fed into the network, which is computationally expensive. \\ \hline [PERSON] et al. [2] & The impact of compression & The authors deduce that video compression adversely affects detection accuracy, in particular the FaceSwap subset of FF++. \\ \hline [PERSON], [PERSON], and [PERSON] [38] & Poor illumination within the frame & Qualitative observations state that the illumination within the frame influences detection accuracy. \\ \hline [PERSON] et al. [39] & Does not consider the audio stream within the deepfake & The model accounts for only one source of inconsistency in deepfake videos, not accounting for audio. \\ \hline [PERSON] and [PERSON] [40] & Generalisability & The model is trained on a very small dataset, limiting its applicability to unseen data. \\ \hline [PERSON] et al. [41] & Occlusions & Facial occlusions impact accuracy and result in a larger frame rate being required. \\ \hline [PERSON] et al. [42] & Hybrid model complexity & Combining the CNN-LSTM with GRU increases the training process time and complexity. \\ \hline [PERSON] et al. [43] & Same training and testing dataset & Using limited test data of the same dataset limits generalisability. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Challenges of video deepfake detection tools in the selected publications.