Techniques

Generative Adversarial Network (GAN)

Definition

A framework consisting of two neural networks—a generator and a discriminator—that compete against each other. The generator creates synthetic data; the discriminator tries to distinguish real from fake. GANs produce highly realistic images and are also studied in the context of deepfake detection.

Related Terms

Diffusion Model

A class of generative models that learn to reverse a gradual noise-addition process to produce high-quality images, audio, or video from random noise. Diffusion models power leading image generators such as Stable Diffusion and DALL-E 3.

Generative AI

AI models capable of creating new content—text, images, code, audio, or video—rather than just classifying existing data. Generative AI is driving a productivity wave in enterprise workflows but also introduces governance challenges around accuracy and intellectual property.

Deepfake Detection

AI-powered methods for identifying media—images, video, audio—that have been synthetically generated or manipulated using generative AI. Deepfake detection is increasingly important for trust infrastructure in elections, financial services, and media.

Knowing the Terms Is Step One. Applying Them Is Step Two.

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