# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 2 Video Deepfake Detection with CNN-LSTM
### Deepfake Creation and Detection  
Deepfakes are hyper-realistic photos, videos, or audio recordings of humans that are digitally manipulated using ML methods. The technology can falsely depict people talking, altering their mannerisms, their movements, and their facial expressions despite the real person not conducting these actions, and cannot always be detected by the human eye. Often, common targets are celebrities or influential figures, whose image is widespread to the public, commonly in the form of traditional and social media. By using a large dataset of images and videos, ML can be utilised to create and apply digital manipulations [3]. GAN is the most common technique employed to generate different types of deepfakes, which is an unsupervised learning algorithm that deals with two subnetworks: trained adversarial with a generator that generates new examples and learns class distribution and a discriminator that classifies input data as real or fake. The generator takes random noise as input to generate an image before the discriminator outputs a probability between zero and one of genuineness. When the discriminator incorrectly classes a fake image as real, the network can generate realistic data [4]. Another technology utilised for creating deepfakes is a Variational Autoencoder (VAE), which consists of an encoder and decoder. The encoder transforms high-dimensional input data into a distribution over latent space whilst the decoder samples from the posterior distribution to capture the variability in the data to generate diverse outputs [5]. Deepfake generation can be categorised into three categories: attribute manipulation, identity swap, and face synthesis. Facial attributes refer to the visual characteristics of faces and can be defined as the inherent properties of human faces, which are categorical and interpretable and include human features such as eyes, lips, noses, beards, hair, and material objects such as glasses [6]. Facial attribute manipulation technology alters appearance by changing facial properties, for example, the modification of hair colour from brown to blonde using a GAN is an example of attribute manipulation, generating a new fake image of a human who does not technically exist. Several studies recognise identity swap as one of the most common deepfake techniques [7], referring to the process of swapping a human face in a source image to a target image. Often, an autoencoder is deployed, which is a type of Neural Network (NN) that uses unsupervised learning to turn inputs into outputs [8]. A face must be detected in both the source and the target media in the pre-processing stage as facial attributes are identified and blended into the target face before overlapping the regions [9]. Alternatively, face synthesis refers to the creation of non-existent faces that are purely computer synthesised. Combining CNN and GANs is a common creation method, where the network classifies images with a pre-trained discriminator and generator that manipulates the attributes of the face to generate images [10]. Like attribute manipulation, segmentation mapping can be used as input to generate completely synthetic faces in pictures or videos [11]. However, control over which facial attributes are manipulated remains limited. To detect deepfake content using unsupervised learning, a feature extraction process is required to determine what facial attributes and external elements can be used to detect whether a face is real or a deepfake. This process involves gathering a large number of samples, selecting some for training, cutting the video into sequential frames, re-scaling the images to be of the same proportions and sizes, and ensuring they all contain the desired features. During the feature extraction stage, facial landmarks are plotted which can also be used for deepfake detection.