AI deepfake content is generated using machine learning models that map one set of facial or audio signals to another while preserving identity characteristics. The pipeline begins by preprocessing the source material: detecting faces, aligning them, extracting keypoints, and normalizing lighting or color. These processed inputs are then passed into the model, which may be an encoder–decoder, GAN, transformer, or diffusion-based architecture. Each frame is generated or modified so that the identity remains recognizable while the pose, expression, or speech pattern adapts to the new context.
During inference, the model uses learned parameters to transform incoming frames in real time or batch mode. For lip-sync deepfakes, the model receives audio-derived features—such as mel-spectrogram slices or phoneme embeddings—and predicts the correct mouth shapes for each corresponding frame. Face-swapping models instead encode the source face into a latent representation and re-render it in the geometry of the target. Quality improvements often come from postprocessing modules that remove artifacts, adjust blending, or sharpen textures.
Vector databases support this process when retrieval or identity verification is needed. For example, embeddings extracted during generation can be stored in Milvus or Zilliz Cloud to track identity similarity across frames. Querying these embeddings helps developers detect drift—cases where the generated face slowly becomes less consistent with the intended identity. Embedding retrieval is also useful when selecting reference frames or conditioning samples in reenactment systems, giving the generation model more accurate context without manually scanning through large datasets.