In 2014, Ian Goodfellow proposed a concept that became influential in artificial intelligence, revolving around two neural networks engaged in a never-ending battle: one responsible for creating images and another trying to detect any lies, called a generative adversarial network (GAN). The first model, a generator, acts like a digital art forger creating convincing fakes, while the second, a discriminator, is an art detective that spots forgeries among real images. As a 2025 paper cited in PubMed indicates, the two-model architecture has become the common way to explain GANs. This technique proved helpful in creating realistic pictures regardless of the lack of training data, and researchers found it could be used for image-to-image translation, image enhancement, and synthetic data generation. Before Goodfellow's proposal, computers were unable to produce believable pictures, and Generative Adversarial Networks provided a new solution through a gaming paradigm. In a 2026 review paper published in Nature, researchers describe how early versions developed the necessary framework for image generation techniques.