AI deepfake refers to synthetic media—usually images, videos, or audio—created by machine learning models that imitate real people with convincing detail. Technically, deepfake systems work by learning how a specific person’s face or voice behaves under different conditions, then generating new outputs that preserve identity while changing expressions, speech, or context. Most implementations rely on neural networks trained to map input frames to target identities, making it possible to swap faces, reenact facial expressions, or synchronize lip movements with arbitrary audio.
A common deepfake architecture includes an encoder–decoder model or a generative adversarial network (GAN). The encoder extracts facial features, compressing them into a latent vector that captures identity and key expressions. The decoder uses that vector to reconstruct a face aligned with the target structure. GAN-based systems refine this output by adding realistic textures, improving lighting, and correcting artifacts. The discriminator inside the GAN learns to distinguish real from synthetic samples, pushing the generator to improve until the output looks authentic. This adversarial process is why GANs are widely used in deepfake pipelines.
Vector databases can be part of this workflow when identity embeddings or frame embeddings need to be stored, compared, or searched. For example, you can extract embeddings for each frame and insert them into a vector database such as Milvus or the managed Zilliz Cloud. This allows developers to run similarity checks across large datasets to ensure consistency of identity, detect anomalies, or monitor model drift during experimentation. Embedding-based search complements the generative model by adding a retrieval and validation layer that supports quality control.