Learn how GANs create convincing fake images, spot the telltale artefacts, play "real or fake", and discover why critical thinking about online media has never been more important!
A neural network that creates fake images, starting from random noise and learning to make them look increasingly realistic.
A second neural network that tries to distinguish real images from fake ones — the "detective" that pushes the generator to improve.
Generator and discriminator compete. Generator gets better at faking; discriminator gets better at detecting. Both improve together.
Modern GANs produce images indistinguishable to the human eye — fuelling misinformation and fake political content worldwide.
You mastered GANs, artefact detection, and AI media ethics!
Two networks compete: Generator creates fakes, Discriminator detects them. Both improve through competition until fakes are indistinguishable.
GAN artefacts: ear asymmetry, hair edge blurring, teeth irregularities, background smoothness, and facial boundary inconsistencies.
When the generator learns to produce only a few types of outputs — it "collapses" into repetitive patterns. A common training failure.
Creating deepfakes of real people without consent is illegal in many countries — including India, where IT Act amendments address synthetic media.
As detection improves, generators improve to fool the detector. This is a genuine adversarial arms race with no clear winner.
The best defence isn't technology — it's humans who question viral media, verify sources, and understand AI limitations.