DEEPFAKE

Deepfakes are so named because they use deep learning technology, a branch of machine learning that applies neural network simulation to massive data sets to create a fake. Artificial intelligence effectively learns what a source face looks like at different angles to transpose it to a target, usually an actor, as if it were a mask. Tremendous advances were made by applying adversarial generative networks (GANS) to pit two AI algorithms against each other, one creating the fakes and the other qualifying their efforts, teaching the synthesis engine to make better fakes.
It is a technology invented in 2014 by Ian Goodfellow, a Ph.D. student working at Apple. Most deep fake technology is based on adversarial generative networks (GANs).
GANs allow algorithms to go beyond data classification to generate or create images. This occurs when two GANs try to fool each other into thinking that an image is “real.” With just one image, an experienced GAN can create a video clip of that person.

Fundamentally, the system can initialize the parameters of both the generator and the discriminator in a specific way for each person so that the test can be based on a few images and performed quickly, despite the need to adjust tens of millions of parameters, this approach allows learning highly realistic and personalized talking head models of new people and even portrait paintings.
Simple…