# Comparison of Deepfake Detection Techniques through Deep Learning
## 1 Introduction  
In the last few years, cybercrime, which accounts for a 67% increase in the incidents of security breaches, has been one of the most challenging problems that national security systems have had to deal with worldwide [1]. Deepfakes (i.e., realistic-looking fake media that has been generated by deep-learning algorithms) are being widely used to swap faces or objects in video and digital content. This artificial intelligence-synthesized content can have a significant impact on the determination of legitimacy due to its wide variety of applications and formats that deepfakes present online (i.e., audio, image and video). Considering the quickness, ease of use, and impacts of social media, persuasive deepfakes can rapidly influence millions of people, destroy the lives of its victims and have a negative impact on society in general [1]. The generation of deepfake media can have a wide range of intentions and motivations, from revenge porn to political fake news. [PERSON], an investigative journalist in India, became a target of this practice when a deepfake sex video showing her face on another woman's body was circulated on the Internet in April 2018 [2]. Deepfakes have also been published to falsify satellite images with non-existent landscape features for malicious purposes [3]. There are numerous captivating applications of deepfakery in video compositing and transfiguration in portraits, especially in identity protection as it can replace faces in photographs with ones from a collection of stock images. Cyber-attackers, using variousstrategies other than deepfakery, are always aiming to penetrate identification or authentication systems to gain illegitimate access. Therefore, identifying deepfake media using forensic methods remains an immense challenge since cyber-attackers always leverage newly published detection methods to immediately incorporate them in the next generation of deepfake generation methods. With the massive usage of the Internet and social media, and billions of images available on the Internet, there has been an immense loss of trust from social media users. Deepfakes are a significant threat to our society and to digital evidence in courts. Therefore, it is highly important to obtain state-of-the-art techniques to identify deepfake media under criminal investigation. As demonstrated in Table 1 (inspired by the figure presented in [1]), tampering of evidence, scams and frauds (i.e., fake news), digital kidnapping associated with ransomware blackmailing, revenge porn and political sabotage are among the vast majority of types of deepfake activities with the highest level of intention to mislead [1]. The first deepfake content published on the Internet was a celebrity pornographic video that was created by a Reddit user (named deepfake) in 2017. The Generative Adversarial Network (GAN) was first introduced in 2014 and used for image-enhancement purposes only [4]. However, since the first published deepfake media, it has been unavoidable for deepfake and GAN technology to be used for malicious uses. Therefore, in 2017, GANs were used to generate new facial images for malicious uses for the first time [5]. Following that, there has been a constant development of other deepfake-based applications such as FakeApp and FaceSwap. In 2019, Deepnude was developed and provided undressed videos of the input data [6]. The widespread strategies used to manipulate multimedia files can be broadly categorized into the following major categories: copy-move, splicing, deepfake, and resampling [7]. Copy-move, splicing and resampling involve repositioning the contents of a photo, overlapping different regions of multiple photos into a new one, and manipulating the scale and position of components of a photo. The final goal is to manipulate the user by conveying the deception of having a larger number of components in the photograph than those that were initially present.