Technology is touching the skies, and these days, there is a lot of buzz around deep fake technology. Here’s everything simplified for you.

Deepfake technology seems like a complicated topic, but today, we have simplified the concepts for you.

The technology is a method that manipulates video content with the use of high-powered computers and with the help of deep learning. 

What happens due to the deepfake technology? Well, the technology is so smart that it can create realistic-looking videos of an event that never actually happened. Sounds amazing yet scary, right?

Deepfake technology- EXPLAINED

Let us first understand the term “deepfake”. The term has been derived from the underlying technology “deep learning”. It is a type of AI. Deep learning algorithms. These algorithms train themselves the ways to solve problems in cases when huge sets of data are made to use to swap faces in digital content and video in order to create fake media that looks super real.

There are multiple methods to create deepfakes. However, the most popular and widely used these days is the use of deep neural networks that involve autoencoders that make use of a face-swapping technique. To get that done, one first requires a target video to be used as the basis of deefake. Next, a collection of video clips of the individual one wishes to insert in the target are made to use.

It is interesting to note that these videos can be entirely unrelated. Which means that the target might be an actor from distant lands, and you can still create a deepfake for it.

What is a deepfake and how does it work?

The term "deepfake" comes from the underlying technology "deep learning," which is a form of AI. Deep learning algorithms, which teach themselves how to solve problems when given large sets of data, are used to swap faces in video and digital content to make realistic-looking fake media. 

There are several methods for creating deepfakes, but the most common relies on the use of deep neural networks involving autoencoders that employ a face-swapping technique. You first need a target video to use as the basis of the deepfake and then a collection of video clips of the person you want to insert in the target. 

The videos can be completely unrelated; the target might be a clip from a Hollywood movie, for example, and the videos of the person you want to insert in the film might be random clips downloaded from YouTube. 

The autoencoder is a deep learning AI program tasked with studying the video clips to understand what the person looks like from a variety of angles and environmental conditions, and then mapping that person onto the individual in the target video by finding common features. 

Another type of machine learning is added to the mix, known as Generative Adversarial Networks (GANs), which detects and improves any flaws in the deepfake within multiple rounds, making it harder for deepfake detectors to decode them. 

How are deepfakes used?

While the ability to automatically swap faces to create credible and realistic looking synthetic video has some interesting benign applications (such as in cinema and gaming), this is obviously a dangerous technology with some troubling applications.

Are deepfakes only videos?

Deepfakes are not limited to just videos. Deepfake audio is a fast-growing field that has an enormous number of applications. 

Realistic audio deepfakes can now be made using deep learning algorithms with just a few hours (or in some cases, minutes) of audio of the person whose voice is being cloned, and once a model of a voice is made, that person can be made to say anything, such as when fake audio of a CEO was used to commit fraud last year. 

Deepfake audio has medical applications in the form of voice replacement, as well as in computer game design – now programmers can allow in-gamer characters to say anything in real time rather than relying on a limited set of scripts that were recorded before the game was published.