Generative Adversarial Networks (GANs) and other generative AI models can create photorealistic images from scratch or alter existing images in ways that blur the line between reality and fabrication.

Generative Adversarial Networks (GANs), a class of AI models, consist of two competing neural networks: a generator that creates synthetic images and a discriminator that tries to distinguish them from real images. Through this adversarial process, GANs learn to produce incredibly realistic, yet entirely fabricated, photographs of faces, landscapes, or objects. This technology has expanded beyond mere creation to sophisticated manipulation, enabling 'deepfakes' that can convincingly alter facial expressions, swap identities, or even generate entire video sequences. Furthermore, generative fill capabilities, as seen in tools like Adobe Photoshop's Generative Fill, allow users to describe desired changes using text prompts, and the AI will generate and seamlessly integrate new content into the image, such as adding objects, extending backgrounds, or changing weather conditions. This represents a paradigm shift from editing existing pixels to generating entirely new ones based on conceptual input.