Deepfakes are an AI technology that makes it look like someone said or did something they never actually said or did. Youโve probably seen a few online. These videos may look real, but they are completely fake. Behind this technology is a powerful deep learning model called a Generative Adversarial Network, or GAN for short.
To me, GANs work like a fascinating game, especially the way theyโre trained. Come with me, let me explain.
A GAN is made up of two networks:
They act like two players in a cat and mouse game, each one trying to outsmart the other.
At first, the Discriminator is trained on real data (the training data). Once it has a good understanding of what real data looks like, the Generator is brought in.
Now hereโs the fun part. The Generator is not trained with the real data. Instead, it is given some random noise (just numbers) as input. Its challenge is to turn that random noise into something so realistic that it fools the Discriminator into thinking it is real data.
๐ฆ๐ผ, ๐ต๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ฒ ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ผ๐ฟ ๐ฒ๐๐ฒ๐ป๐๐๐ฎ๐น๐น๐ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐ฒ ๐ฑ๐ฎ๐๐ฎ ๐๐ต๐ฎ๐ ๐น๐ผ๐ผ๐ธ๐ ๐ฟ๐ฒ๐ฎ๐น ๐ฒ๐ป๐ผ๐๐ด๐ต?
Thatโs the beauty of the setup. Every time the Discriminator catches a fake, the Generator learns from that mistake and tries again. The Discriminator gets better at spotting fakes, and the Generator gets better at creating more convincing ones. They both improve by competing with each other.
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