The term deep fake has been popping up all over the web – as well as the news – recently, but what exactly is it? Let’s take a look…
The simulation of reality by computers has improved throughout time. In lieu of the genuine settings and props that were historically frequent, modern film, for instance, extensively depends on computer-generated sets, scenery, and people, and most of the time these sequences are almost indistinguishable from reality.
Having recently spent a lot of time on the news for a number of reasons – good and bad – deep fakes are the most recent advancement in computer images, produced when artificial intelligence (AI) is trained to swap out one person’s appearance for another in a recorded video.
This article will provide all the details you need to catch up if you’re unclear about what deep fake technology is or the applications it may serve when paired with today’s technology.
What Is A Deep Fake?
Deep fakes are fake videos produced by computers. They produce original content by patching together images to show things that never happened, including remarks or actions.
The results may also be quite persuasive and are distinct from other forms of misleading information in the sense that they are exceedingly challenging to identify.
Deep fakes are material that often take the form of video, however, they may also include audio. Deep learning is used to build, modify, or synthesise them and as a result, it tricks viewers or listeners into accepting a fake event or message.
How Do Deep Fakes Work?
Two AI algorithms that are in competition with one another, the generator and the discriminator, are used to produce deep false content. The discriminator is tasked with determining if the fake multimedia material produced by the generator is genuine or manufactured.
A generative adversarial network is what is created when the generator and discriminator work together (GAN). The discriminator gains invaluable knowledge on how to enhance the next deep fake each time it correctly recognises material as being fake.
Finding the intended output and producing a training dataset for the generator are the initial steps in setting up a GAN. Video clips may be supplied to the discriminator after the generator starts producing output at a level that is acceptable.
The discriminator becomes more adept at recognising phoney video clips as the generator does. The generator, on the other hand, improves at producing phoney videos as the discriminator does at recognising them.
What Can Deep Fakes Be Used For?
Deep fake content offers a diverse range of uses and is often associated with adult and scam content. Despite this association, deep fake content also serves a variety of beneficial and lawful objectives.
The following is a short list of the many advantages that society may reap through the use of deep fake technology:
The main principle of reflection, stretching, twisting, and appropriation of genuine events in comedy or parody may be accurately realised with the help of deep fakes. Extraordinary possibilities in the entertainment industry may result from AI-Generated synthetic media, and we are already seeing independent producers on YouTube make use of this potential in significant numbers.
Another excellent use for synthetic speech is audiobook narration. The audio version of the author’s book may be produced using the author’s synthetic voice font. To increase the audience for their material, businesses might utilise synthetic voice-overs performed by the same person in other languages.
AI-generated images and imagery have the potential to speed up the development of video games. A hybrid gaming environment developed using deep fakes was shown by Nvidia, and the company is preparing to release it shortly.
Deep fake technology opens up a wide range of opportunities in the field of teaching. For a long time, schools and instructors have used audio, video, and other forms of media in the classroom. An instructor may use deep fakes to give compelling lessons that go beyond the scope of conventional visual and media forms.
In order to create a more engaging and dynamic classroom, artificial intelligence-generated synthetic media may revive historical personalities. A voice-over and film of a historical character or a synthetic movie of reenactments may have more effect, connection, and effectiveness as a teaching tool.
In authoritarian and repressive regimes, artificial media may assist journalists and human rights advocates in maintaining their anonymity. For citizen journalists and activists, using technology to report on crimes on conventional or social media may be immensely powerful. To safeguard their privacy, voices and faces may be made anonymous using deep fake technology.
Is Deep Fake Technology Dangerous?
Although the public does not generally have a good understanding of deep fakes, many people are beginning to dread the concept. It’s difficult to believe anything you see these days.
Deep fakes are now making it more difficult to believe what you see. If we ever expect to lessen the harm that deep fakes may do, people must become informed of the reality surrounding them.
As previously stated, deep fake technology is often used to create adult material. Usually, this adult content will feature a celebrity’s face who of course has not given permission for it to be used, which can be devastating to the person involved.
The capability of using these films to blackmail victims and the creation of “sockpuppets” are two more serious problems with deepfakes. If sockpuppets exist, creators may provide any amount of video proof of it, even if they don’t. Then, without facing any repercussions, internet abusers may use these sockpuppets to wreak havoc online.
One basic example would be if a fraudster created a video using your image and sent it to your grandparents asking for money to get you out of a situation. Your grandparents will then send money over to the fraudster believing they are helping you.