# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion

Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Citation** & **Summary of Contribution** & **Limitations** \\ \hline \hline \multirow{6}{*}{[PERSON] et al. [60]} & The paper uses a multi-stream CNN-LSTM network with contrastive performing the best using EfficientNetB7 architecture on the FF++ dataset with 97.3\%. Introducing contrastive loss allowed the model to learn features independent of the deepfake generation method, which influences the FF++ dataset. & The proposed model struggles to detect expression-swapping deepfakes compared to face-synthesis deepfakes due to the subtler visual artefacts present in expression swaps. \\ \hline \multirow{6}{*}{[PERSON] et al. [61]} & The paper combines optical flow during feature extraction, VGG16 CNN, and LSTM; this model’s performance fares & The proposed model does not perform as well on the DFDC dataset, with low frame frames (20) producing a 0.5 accuracy. \\ \cline{1-1}  & The paper combines ResNeXt-50-32x4d architecture with LSTM and concludes the model has higher performance metrics when trained for 40 epochs over 20 epochs when using a combination of FF+, DFDC, and Celeb-DF. & The proposed model selects the first 150 sequential frames; therefore, deepfake content after the 150 th frame is ignored. \\ \hline [PERSON], [PERSON], and [PERSON] [62] & The paper proposes a model that combines ResNeXt architecture with LSTM. The results demonstrate an accuracy of 94\% on the Celeb-DF dataset. & The proposed model uses a small training and testing set so it is not very generalisable. \\ \hline \multirow{6}{*}{[PERSON] et al. [63]} & The paper compares backbone networks using the DFDC dataset and demonstrates that EfficientNet-B1 wrapped in a time-distributed layer followed by an LSTM layer resulted in the highest AUC sore and accuracy with 14 million fewer parameters than XceptionNet and InceptionNet backbones, with performances of 86\% and 92\%, respectively. & Because the model was trained using only a portion of the DFDC dataset, its accuracy decreased when merged with the FF++ dataset. \\ \hline \multirow{6}{*}{[PERSON] and [PERSON] [65]} & The paper proposes a model that performed well with an accuracy of 97.25\% on compressed videos (c = 23) at 13 epochs, where it became constant. The accuracy increased by 10\% from 1 epoch to 13 epochs. & Because the model was trained using only a portion of the DFDC dataset, its accuracy decreased when merged with the FF++ dataset. \\ \hline \multirow{6}{*}{[PERSON] et al. [66]} & The paper integrates Xception, LSTM, and the late fusion approach for combining outputs generated from facial regions to conclude that late fusion does not increase the AUC score using the Celeb-DF or FF++ datasets but provides a high-performing baseline for when only specific facial landmarks are deepfakes. \\ \cline{1-1}  & The paper proposes adding a soft-attention mechanism based on weights to a CNN with LSTM which reduces the network’s calculation costs and increases classification accuracy. \\ \hline \multirow{6}{*}{[PERSON] and [PERSON] [67]} & The paper uses EfficientNet architecture with LSTM on the DFDC and FF++ datasets and concludes that training a model using residual image autoencoders to capture high-frequency details and improve reconstruction quality gives a higher classification accuracy of 94.75\%. \\ \hline \multirow{6}{*}{[PERSON] et al. [68]} & The paper tests ResNeXt-50-32x4d CNN with 3 LSTM layers. The results showed that accuracy stabilised at 60 frames on the Celeb-DF and DFDC datasets at 97\%, but when combined with FF++, performance capped at 93\% at 100 frames. \\ \hline \multirow{6}{*}{[PERSON] [69]} & The paper proposed a VGG16 and ResNet-50-32x4d-based CNN-LSTM model that is trained on the MRL eye image dataset and tested on the UADFV dataset. The overall detection accuracy on real videos was over 90\%. \\ \cline{1-1}  & The paper proposed a VGG16 and ResNet-50-32x4d-based CNN-LSTM model that is trained on the MRL eye image dataset and tested on the UADFV dataset.