Artificial intelligence (AI) is rapidly changing the world, and one of the most concerning developments is the rise of deep fakes. Deep fakes are videos or audio recordings that have been manipulated using AI to make it appear as if someone is saying or doing something that they never actually said or did.

Deep fakes are becoming increasingly sophisticated, and it is becoming more and more difficult to tell them apart from real videos and audio recordings. This has raised a number of concerns about the potential for deep fakes to be used to spread misinformation, damage reputations, and even commit fraud.

What are deep fakes?

Deep fakes are created using a type of AI called a generative adversarial network (GAN). GANs are two neural networks that are trained to compete against each other. One neural network, the generator, is tasked with creating fake images or videos. The other neural network, the discriminator, is tasked with distinguishing between fake and real images or videos.

The generator is trained on a dataset of real images or videos. The discriminator is also trained on a dataset of real images or videos, but it is also trained on the fake images or videos that the generator has created.

The generator and discriminator are then pitted against each other in a game-like setting. The generator tries to create fake images or videos that are so realistic that the discriminator cannot tell them apart from real images or videos. The discriminator tries to distinguish between fake and real images or videos.

Over time, the generator and discriminator become better and better at their respective tasks. The generator becomes better at creating fake images or videos that are indistinguishable from real images or videos. The discriminator becomes better at distinguishing between fake and real images or videos.