# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
The most prominent dataset used within the selected papers was the DFDC dataset, which was used 25 times. DFDC is a deepfake dataset created by Facebook for a deepfake detection challenge with a partial set containing 4113 deepfake videos and is based on an unknown creation algorithm [78]. All the faces collected are actors with informed consent, which the Celeb-DF and FaceForensics++ datasets lack. The results from [PERSON] et al. [43] determine that CNN-LSTM models can achieve strong accuracy on the DFDC dataset alone, at 96% on unseen data, but it uses a very small sample size and therefore is not generalisable. The FaceForensics++ (FF++) dataset [79] contains 1000 videos split into 509,914 images (frames) sourced from YouTube videos and were manipulated using Face2 Face, FaceSwap, and neural texture and deepfake tools to create the deepfake content. This was the second most prominent dataset utilised by authors, with 24 uses. The subjects within the frames are forward-facing and do not contain occlusions to maximise detection. The results from [PERSON] et al. [59] and [PERSON] et al. [60] both concluded that their models performed \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Citation** & **ResNet** & **ResNeXt** & **Xception** & **InceptionNet** & **VGG-16** & **EfficientNet** & **MobileNet** & \
\begin{tabular}{c} **Image** \\ **Net** \\ \end{tabular} &
\begin{tabular}{c} **ConvLSTM** \\ \end{tabular} & **Original** \\ \hline [PERSON] et al. [14] & x & x & x & x & x & x & & & & x & x & x & x \\ [PERSON] et al. [38] & x & x & x & x & ✓ & x & x & x & x & x \\ [PERSON] [55] & x & x & x & x & x & x & x & x & x & ✓ \\ [PERSON] et al. [56] & x & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON], [PERSON], and & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] [57] & x & x & x & x & x & x & x & x & x & x \\ [PERSON] et al. [58] & x & x & x & x & ✓ & x & x & x & x & x \\ [PERSON] et al. [59] & x & x & x & x & ✓ & x & x & x & x & x \\ [PERSON] et al. [60] & x & x & x & x & x & ✓ & x & ✓ & x & x & x \\ [PERSON] et al. [61] & x & x & x & x & x & x & x & x & x & ✓ \\ [PERSON] et al. [25] & x & ✓ & x & x & x & x & x & x & x & x & x \\ [PERSON], [PERSON], and & & & & & & & & & & & \\ [PERSON] [62] & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] et al. [63] & x & x & x & x & x & x & ✓ & x & x & x \\ [PERSON] [64] & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] and & & & & & & & & & & & \\ [PERSON] [65] & x & x & ✓ & x & x & x & x & x & x & x \\ [PERSON] et al. [66] & x & x & x & x & x & ✓ & x & x & x & x \\ [PERSON] and [PERSON] [67] & x & x & x & x & x & ✓ & x & x & x & x & x \\ [PERSON] et al. [68] & x & ✓ & x & x & x & x & x & x & x & x \\ [PERSON] [69] & ✓ & x & x & x & ✓ & x & x & x & x & x \\ [PERSON] et al. [70] & x & x & x & ✓ & x & x & x & x & x & x \\ [PERSON] et al. [71] & x & ✓ & x & x & x & x & x & x & x & x \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._the lowest on the Neural Textures subset of the FF++ dataset, indicating that the models perform [80] strongest on GAN-generated deepfakes. As this is the primary creation method of deepfakes, this causes little concern. [PERSON] et al. [68] determined that their model had the strongest performance using 100 frames on the FF++ dataset at 97.89%. Combining the dataset with Celeb-DF and DFDC during testing caused performance to drop to 93%. The Celeb-DF dataset contains 590 videos from YouTube and 5639 deepfake videos of celebrities split into over 2 million frames. The dataset was utilised 16 times within the training and testing phases in the selected papers. The dataset contains 59 celebrities of various ages, genders, and ethnicities to maximise diversity and detection. The deepfakes are generated using a deepfake synthesis algorithm with a high resolution, reduced colour mismatch, and reduced temporal flickering using a Kalman smoothing algorithm [81]. Research from [PERSON] et al.