# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
[38] explores how the Celeb-DF dataset has a large class imbalance, with the majority of the content being deepfake, so augmentation and resampling has to occur to ensure an equal balance between real and deepfake videos during training. Therefore, studies that use this dataset alone will learn fewer deepfake features during the feature extraction process. This is reinforced by [PERSON], [PERSON], and [PERSON] [62], who although 94.21% detection accuracy was reached, concluded that the dataset is not very generalisable. Deepfake-TIMIT is a dataset of deepfakes generated from GANs, consisting of 10 videos of 32 people, with low-quality and high-quality options [82]. The dataset has no manipulation of the audio channel; therefore, it is not a good dataset if an audio-visual detection method is desired. However, as most deepfakes are generated using GANs, the dataset is a good baseline for video detection. This dataset appeared three times within the papers. Experimental results from [PERSON] et al. [51] on different baselines determined that the Deepfake-TIMIT dataset produced a consistently high false alarm rate and missed detection rate compared to the DFD and FF++ datasets. A robust model must not have a high false-positive rate; therefore, this dataset is not suitable for training models alone. The UADFV dataset has 98 videos, with 49 real and 49 deepfake videos [83], which is not computationally expensive but is not a large dataset to train from. The real videos are collected from YouTube and the deepfakes are generated using FakeAPP [84], which were utilised twice within the papers. This dataset is small and therefore will learn intricate  
Figure 7: Common datasets used in video deepfake detection. feature patterns that are not generalisable to unseen content. Authors have also created original datasets to use for tests, with several authors collecting YouTube videos. Although this is good for gathering compressed data, which applies to the general use of deepfakes, there may be concerns surrounding data collection consent. The results from [PERSON] et al. [50] failed to detect deepfake content from YouTube videos, which is viewed as the 'worst case', although it is the most readily available deepfake content online. Figure 8 is a Sankey diagram, demonstrating the usage of each dataset and the correlation of the datasets each paper used. Several datasets were used per model in many cases, as they tested and trained on different datasets. The use of different datasets causes limitations for model performance and generalisability. As observed, datasets like DFDC and FF++ are widely used due to their size and diversity but they differ in manipulation types used to generate deepfake content, therefore impacting the quality of the videos that the model trains on and their detection. These variations make it difficult to compare results across models that have been trained on different datasets, as each model learns a specific subset of deepfake techniques, for example, [PERSON] et al. [61] achieve 91% accuracy on the FF++ dataset, but 61% on DFDC. If models are only trained on one dataset, they can struggle to generalise to others during testing, particularly when those datasets contain deepfakes generated using unfamiliar methods (VAE, GAN) or contain elements such as occlusions, facial rotations, or a change in resolution. Combining datasets to enhance generalisability exposes the CNN-LSTM model to a larger set of real and deepfake videos to extract features from but could introduce overfitting or a class imbalance to a specific type of deepfake generation method, therefore requiring even sets of generation methods to be input into the model. Figure 8: Video deepfake datasets used within the selected publications [2,14,25,32,34,36,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71]. A primary reason for the lack of generalisability is that these models often overfit the specific features of the datasets they are trained on.