SECAI Core · phase 2 of 15
Generative adversarial network
A type of artificial intelligence model in which two neural networks compete against each other to generate realistic synthetic data, such as images or text.
The Explain card
- Plain English
- A generative adversarial network (GAN) pits two neural networks against each other: a generator makes synthetic data and a discriminator tries to spot the fakes. Each improves by trying to beat the other.
- Example
- A GAN trained on employee photos produces realistic faces for a fake social media profile used in a social engineering campaign against the company.
- Why it matters
- GANs helped create the deepfake era. Understanding the generator-versus-discriminator loop also explains why detection is an arms race: every better detector trains a better forger.
- Hook
- Forger and art expert locked in a room until the forger wins.
Where it sits in the deck
Phase 2: How Models Are Built: Architectures and Learning Mechanics
With the paradigms named, zoom in on the architectural building blocks that turn data into trained artifacts.
