Deepfake AI Face Generation

The traditional art industry is changing rapidly as modern artists continue to incorporate advanced technologies into their work. One of the most common technologies being incorporated into art is the use of artificial intelligence (AI) art generators to create images and other pieces of art. With a reliable AI art generator like NightCafe Creator, you can easily generate images with a neural network.

Images generated with this tool are so real that you often can’t differentiate them from hand-drawn images if you’re not a professional art appraiser. The software also allows you to generate images with human-like faces through Deepfake AI face generation–it’s one of the easiest ways of creating your own face with AI.

With the new technology, artists can create images with natural faces from simple text prompts and existing images. If you’ve been struggling to create professional and unique digital artworks that will interest potential collectors and art lovers, you should consider incorporating an art generator that will enable you to generate real faces of your subjects.

Creating faces using an AI face generator from prompt texts and existing images will catapult your career in the art industry because it compensates for the times when creativity or precision is missing in your pieces. Before you begin to incorporate Deepfake AI face generation into your artwork, you need to understand what it is and how it works.

What’s Deepfake AI?

Deepfake AI is a form of AI used to develop convincing images and other simulated pieces of digital art. The term Deepfake describes both the programs used and the content created; it’s a combination of deep learning and fakeness.

A perfect example of the Deepfake AI face generation technique is when David Beckham, a renowned English soccer player, was used by a nonprofit health organisation in the United Kingdom to deliver an anti-malaria campaign message. The organisation used the Deepfake method to deliver the message in different languages.

Unfortunately, there are unscrupulous people who have been using this technology for malicious purposes, such as spreading misleading information in a way that appears as though it is coming from trusted sources. Deepfake propaganda, for example, is being used to manipulate elections. This is why many companies that are developing AI art generators that use Deepfake face generation technology are putting very strict terms of use in place.

How Does Deepfake AI Work?

Images generated through the Deepfake AI generation technique are developed with two competing algorithms: the generator and the discriminator. The generator algorithm is responsible for creating the fake images, while the discriminator determines how real or fake the generated images are.

Both algorithms form a generative adversarial network (GAN), which is a group of machine learning structures necessary for generative modelling through deep learning techniques such as neural networks. Generative modelling involves the automatic discovery and learning of patterns and regularities in inputs to generate new examples that could appear to be drawn from the primary dataset.

In short, a GAN is an ingenious way of training a program like an AI art generator (generative model) by framing an input, as a supervised learning problem, with the generator and discriminator algorithms or submodels. These algorithms are trained in an adversarial or zero-sum process to deceive the discriminator to allow the generator to produce plausible results.

Whenever the discriminator algorithm identifies fabricated information, it gives the generator valuable data on how to enhance the next Deepfake. The generative model establishes a GAN by identifying the preferred output and creating a learning dataset for the generator. The generator then starts to create a plausible output and feeds the data to the discriminator.

As the discriminator becomes better at identifying fabricated data, the generator becomes better at generating plausible Deepfakes. Conversely, as the generator becomes better at generating believable Deepfakes, the discriminator gets better at identifying them–this is the whole idea behind the Deepfake AI face generation method.