GANs are deep neural net architectures that consist of two networks pitted against each other (hence the name "adversarial"). Ian Goodfellow et al. introduced GANs in a paper (see more at https://arxiv.org/abs/1406.2661v1). In GANs, the two main components are the generator and discriminator.
The Generator will try to generate data samples out of a specific probability distribution, which is very similar to the actual object. The discriminator will judge whether its input is coming from the original training set or from the generator part.