# Comparison of Deepfake Detection Techniques through Deep Learning
## 2 Literature Review
### Deepfake Detection Algorithms  
Deepfake detection techniques aim to conceal revealing traces of deepfakes by extracting semantic and contextual understanding of the content. Research in the field of media forensics provides a wide range of imperfections as indicators of fake media: face wobble, shimmer and distortion; waviness in a person's movements; inconsistencies with speech \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Real**} & \multicolumn{2}{c}{**Fake**} \\ \hline
**Dataset** & **Video** & **Frame** & **Video** & **Frame** & **Generation Method** & **Release Date** & **Generation Group** \\ \hline UADFV & 49 & 17.3K & 49 & 17.3K & FakeAPP & 11/2018 & 1 st \\ \hline DF-TIMIT & 320 & 34K & 320 & 34K & Faceswap-GAN & 12/2018 & 1 st \\ \hline “Real \& Fake Face Detection & 1081 & 405.2K & 960 & 399.8K & Expert-generated high-quality photoshopped & 01/2019 & 2 st \\ \hline FaceForensics++ & 1000 & 509.9K & 1000 & 509.9K & DeepFakes, Face2 Face, FaceSwap, NeuralTextures & 01/2019 & 2 nd \\ \hline DeepFakeDetection & 363 & 315.4K & 3068 & 2242.7K & Similar to FaceForensics++ & 09/2019 & 2 nd \\ \hline DFDC & 1131 & 488.4K & 4113 & 1783.3K & Deepfake, GAN-based, and non-learned methods & 10/2019 & 2 nd \\ \hline Celeb-DF & 590 & 225.4K & 5639 & 2116.8K & Improved DeepFake synthesis algorithm & 11/2019 & 2 nd \\ \hline “140K Real \& Fake Faces & 70K & 15.8M & 70K & 15.8M & StyleGAN & 12/2019 & 2 nd \\ \hline DeeperForensics & 50,000 & 12.6M & 10,000 & 2.3M & Newly proposed end-to-end face swapping framework & 06/2020 & 2 nd \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of publicly available deepfake datasets. and mouth movements; abnormal movements of fixed objects such as a microphone stand; inconsistencies in lighting, reflections and shadows; blurred edges; angles and blurring of facial features; lack of breathing; unnatural eye direction; missing facial features such as a known mole on a cheek; softness and weight of clothing and hair; overly smooth skin; missing hair and teeth details; misalignment in face symmetry; inconsistencies in pixel levels; and strange behavior of an individual doing something implausible are all the indicators and features used by deepfake detection algorithms [13]. The use of deep-learning techniques and algorithms such as CNN and GAN has made deepfake detection more challenging for forensics models because deepfakes can preserve pose, facial expression and lighting of the photographs [29]. Frequency domain, JPEG Ghost and Error Level Analysis (ELA) are among the first methods that were used to identify manipulation traces on images. However, they are not successful in identifying manipulated images that are generated with deep-learning and GAN algorithms. Neural networks are one of the most widely used methods for deepfake detection. There are some proposals on the usage of X-rays [18], and spectrograms [30] to identify traces of blending and noise in deepfake media. However, such methods cannot detect random noise and suffer from a performance drop when encountering low-resolution images. Deepfakes are implemented mainly using a CNN that generates deepfake images and an encoder-decoder network structure (ED), or GAN [4] that synthesizes fake videos. Deepfake detection techniques focused on anomalies in the face region only can be categorized into holistic and feature-based matching techniques [31]. The holistic techniques, which are mostly used to identify deepfake face images and include Principal Component Analysis (PCA), Support Vector Machines (SVM), and CNN, mainly analyze the face as a whole. These techniques aim at reducing data dimensionality by forming a smaller set of linear combinations of the image pixels that are then fed to a binary classifier to identify authentic and fake images.