# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 5 Results and Discussion
Passing features from the XceptionNet module into bi-LSTM architecture achieved remarkable results, with a double pass achieving a detection accuracy of 99.7%, surpassing the one-directional LSTM with a forward pass in two prominent datasets, FaceForensics++ and Celeb-DF. Similarly, significant advancements were made by employing bi-LSTMs to fuse two bidirectional outputs with convolutional filters, effectively boosting the frame-level detection performance for videos with medium compression rates, increasing the Area Under the Curve (AUC) score from 92% to 99% [14]. Furthermore, the implementation of the Dense-Swish-121 feature descriptor in conjunction with bi-LSTMs showcased exceptional accuracy, surpassing five state-of-the-art CNN LSTM models on the DFDC dataset, reaching an impressive video deepfake detection accuracy of 99.3% [55]. However, using an Xception-based CNN-LSTM model to reduce feature dimensions before the LSTM cells results in the loss of valuable temporal correlations because Xception uses spatial learning, rendering it transparent to temporal correlations within the input sequences [36]. This results in the LSTM cells receiving features that have lost their temporal context and hinders the model's understanding of intrinsic patterns and temporal contexts. In response, the first spatiotemporal attention mechanism with ConvLSTM for deepfake detection was developed to solve the challenges introduced by Xception-LSTM-based algorithms [36]. Incorporating convolutional operations within the LSTM cell allows the model to handle spatial-temporal information effectively. The model's superiority to other state-of-the-art models is due to the spatiotemporal attention mechanisms being introduced before dimensionality reduction and the ConvLSTM considering the structure information of features during temporal modelling by the LSTM. The model outperforms other state-of-the-art methods with 99% detection accuracy on the FaceForensics++ dataset. The second category is the studies that discussed the blinking rate and its effect on the detection of video deepfakes. Several studies have researched using just the eye landmarks to determine whether a video is real or deepfake. The introduction of a long-term recurrent CNN captured the temporal relationship between consecutive eye-blinking frames from an open to a closed state [52]. The findings showed that deepfake videos have a blinking rate ten times lower than real videos, therefore acting as a threshold in conjunction with spatial-temporal detection. Training a ResNet-50 CNN on the MRL eye image dataset determined during the testing stage that the blinking rate was 4.3 times lower than the blinking rate in real videos on the UADFV dataset [69]. The overall detection accuracy on real videos was 92.23% and 98.3% on fake videos; however, a very small sample was used. An extension of this research determined a lack of, or too frequent, blinking can indicate deepfake content [59]. This model used a CNN for feature extraction and then standardised a frame rate for the blinking patterns to result in 255 eye-state probabilities where an eye is fully open or completely shut. The output was fed into an LSTM cell to determine the eye's state before a Fully Connected Network (FCN) denotes its classification. The model's accuracy was highest when trained when the batch size was set to 20 and trained for 105 epochs, achieving 95.57% on the FaceForensics++ dataset. Another category discussed in the literature studies was emotional analysis and its role in video deepfake detection. For example, [PERSON] et al. [39] use a CNN trained to detect human faces with another trained to associate a human emotion within the frame before a three-layered bidirectional LSTM network captures sequential data and a fully connected layer classifies the video as real or deepfake. The model outperforms the performance of the DFDC competition winners using 403 million fewer parameters.