Deep Fake Technology in Media: A literature review

Abstract

Deepfakes have become possible through advances in artificial intelligence, particularly deep learning architectures. These technologies enable the creation of highly realistic audio, video, and imagery using the algorithms capable of imitating the appearance of a person or an object. In general, deepfakes are a technique that uses machine learning to synthesize and manipulate images, videos, and audio. They mainly rely on deep learning models, specifically generative adversarial networks (GANs). This model is based on the process when two neural networks compete and learn how to generate realistic results.

The main goal of this paper is to present an analysis of deepfake technology, focusing on its history and the key mechanisms used to produce deepfakes. In particular, this paper will focus on neural network training and discuss how recent advances in this field have made deepfakes even more realistic. As a result, the emergence of deepfakes poses serious challenges for information verification.

Unfortunately, the use of deepfake technologies in modern society is becoming increasingly common. Deepfakes create various opportunities for misuse as they can be used to create fakes in order to commit criminal acts, including fraud, election tampering, identity theft, extortion, defamation, terrorism, and so on. All these activities make information verification extremely important since the rise of fake news is increasing continuously.

There are various measures implemented in order to mitigate negative effects related to deepfake technology. In addition to the application of machine learning methods, there are various regulations and laws governing the activities of people working with deepfakes.

Furthermore, online services apply various content moderation techniques to prevent people from using deepfake technology illegally.

Some of the emerging methods of preventing and detecting deepfakes include digital watermarking and forensic analysis. There are several methods for detecting fake images and analyzing the content of images and videos. Finally, another promising strategy is public awareness campaigns that inform users about deepfakes.