Deepfake AI mostly learns by watching thousands of real faces, noticing little things like blinking, expressions, and head movements, almost like copying humans naturally.
It then tries to place one person’s face on another person’s video, matching light, angle, and movements so it doesn’t look weird.
An encoder compresses the original face into a small pattern, keeping only the important details needed for recreating a new version later.
A decoder uses that pattern to rebuild a fresh face, replacing the old one in the video while keeping the same actions.
The system adjusts skin color, shadows, brightness, and even tiny highlights, so the swapped face mixes smoothly and doesn’t stand out.
It carefully tracks micro-expressions like eyebrow lifts, quick smiles, and small eye movements to make the final deepfake feel more natural.
Lip-syncing matches mouth movements with the voice, making sure every word almost fits perfectly, otherwise the video will look off.
GANs improve the output by letting one AI create faces and another AI judge mistakes, forcing better and more realistic results.
Finally, editors fix glitches, clean edges, and smooth transitions, giving the deepfake a polished look that appears ready for sharing.