# Comparison of Deepfake Detection Techniques through Deep Learning
## 2 Literature Review
### Deepfake Detection Datasets  
Deepfake detection systems typically leverage binary classifiers to cluster information into real and fake classes. This method requires a great quantity of good-quality authentic and tampered data to train classification models. The first known datasets that had a great impact on the growth and improvement of deepfake detection technologies  
Figure 2: Current deepfake media types and detection techniques.  
were UADFV [21] and DFTIMIT [22]. FaceForensics++ dataset includes 977 downloaded videos from YouTube, provides 1000 sequences of original unobstructed faces, as well as their manipulated versions. The manipulated versions were generated by four methods: Deepfakes, Face2 Face, FaceSwap and NeuralTextures [23]. The DeepFakeDetection dataset (DFD) released by Google in collaboration with Jigsaw contains over 363 original sequences from 28 paid actors in 16 different scenes as well as over 3000 manipulated videos using deepfakes [23]. The Deepfake Detection Challenge (DFDC) dataset [24] published by Facebook is another publicly available large dataset that includes over 100,000 total clips from 3426 actors, produced with deepfake, GAN-based and unsupervised models. Celeb-DF (v2) [25] dataset published by [25] is an extension to Celeb-DF (v1) that contains real and fake videos that are generated via deepfake algorithm by providing images with the same quality as the synthesized videos circulating online. This dataset provides 5639 videos with subjects of different ages, ethnic groups and genders, and their corresponding deepfake videos. The DeeperForensics-1.0 dataset is a large-scale benchmark for face forgery detection that represents the largest face forgery detection dataset by far. This benchmark includes 60,000 videos forming a total of 17.6 million frames generated by an end-to-end face-swapping framework. Furthermore, extensive real-world perturbations are applied to obtain a more challenging benchmark of larger scale and higher diversity [26].  
For our research and analysis, we took the \"Real and Fake Face-Detection\" dataset from Yonsei University [27] that contains expert-generated high-quality PhotoShopped face images. The dataset includes 960 fake and 1081 real images that are composites of different faces, separated by eyes, nose, mouth, or whole face. The second dataset that has been used in this work is the \"140K Real and Fake Faces\" that consists of 70K real faces from the Flickr dataset collected by Nvidia, as well as 70K fake faces sampled from the 1 million fake faces (generated by StyleGAN) that were published by [PERSON] [28]. These two datasets were used to include both GAN-generated images along with expert/human-generated images to provide many good-quality data. All the above-mentioned datasets can be used for image and video classification, segmentation, generation and augmentation of new data. Table 2 represents a cumulative comparison of the mentioned datasets; please note that the rows with a \"_***_\" sign include images only (not videos). Deepfake datasets have been categorized into two generations based on several factors and elements. Considering release time and synthesis algorithms involved in the generation of the data, UADFV and DF-TIMIT are categorized as the first generation. Considering the quality and quantity of the generated data, DFD, DeeperForensics, DFDC, and the Celeb-DF datasets are categorized as the second generation [25].