# Comparison of Deepfake Detection Techniques through Deep Learning
## 2 Literature Review
### Deepfake Detection Algorithms
Feature-based or attention-based matching techniques, however, are used for both deepfake video and image identification, and split the whole face into different regions of focus such as eye, nose, lips, skin, head position, color mismatches, etc. [32]. Holistic techniques are successful in detecting localized deepfake characteristics (i.e., anomalies in the face and jaw region) and can be leveraged to identify specific feature characteristics (eyes, nose, mouth) that could be significant in detection [12]. Convolutional Neural Network (CNN)-based image classification and recognition models have been proven to be trainable to classify manipulated images from authentic ones [33]. [PERSON] et al. [34] aimed to extract and detect fingerprints that represent convolution traces left in the process of generating GAN images using the Expectation-Maximization algorithm. [PERSON] et al. [35] demonstrated that with careful pre- and post-processing and data augmentation, a standard classifier trained on ProGAN, an unconditional CNN generator can be generalized surprisingly well to unseen architectures, datasets, and training methods. CNN have also been trained to detect manipulation techniques such as lack of eye-blinking [36], missing details in eyes from an image [37], and facial wrapping artifacts. Furthermore, CNNs have been shown to be able to capture distinctive traces of generation methods that have worked on further wrapping the faces with high-resolution sources [17]. VGG19 and VGG16 has significantly improved large-scale fake image recognition by increasing the layer depth (23/26 layers) of CNN-based models [38]. [PERSON] et al. [39] presented an improved VGG network, namely NA-VGG, based on image augmentation and noise-level analysis to detect a deepfake face image. The experimental results using the Celeb-DF dataset shows that NA-VGG improved accuracy over other state-of-the-art fake image detectors. [PERSON] et al. [40] demonstrated that VGG-16 has a better performance than the ShallowNet architecture to classify genuine facial images from disguised face images. Furthermore, DenseNet architecture has also been demonstrated to be computationally more efficient with its feed-forward design network, which connects each layer to every other layer [41]. In DenseNet architecture, feature maps of all former layers are used as the input for each layer. DenseNet requires significantly fewer parameters and computation to achieve state-of-the-art performance [33]. [PERSON], [PERSON], [PERSON], and [PERSON] [42] in their work proposed a fake face-image detector based on the novel CFFN, consisting of an improved DenseNet backbone network and Siamese network architecture. Their comprehensive analysis demonstrated that deep features-based deepfake-detection systems such as DenseNet obtain significant accuracy when trained and tested on the same kind of manipulation technique. Feature-based techniques have started identifying the deficiencies of deepfake generation methods such as unnatural eye-blinking patterns and temporal flickering, which gave rise to a more improved generation of deepfake models that were trained on datasets that have addressed the identified deficiencies. [PERSON] et al. [43] demonstrated that facial landmarks could be used to provide an estimate of head posture direction. The work of [44, 45] illustrated that eye pupils' inconsistencies are one of the indicators of fake media. Some studies [46] including audio into the training process have illustrated that the difference between lip movements and voice matching distinguishes real and fake media. There have been some efforts on domain-specific deepfake detection such as [47] that leveraged forensic techniques to model political leaders' facial expressions and speaking patterns; however, it would be a more difficult task to train and generalize such approach for the whole world. Even though feature-based techniques are more robust to deformations, they have been mainly designed to have the best performance on domain-specific datasets.