GAN-based generation¶
Generative adversarial networks (GANs) are a machine learning architecture consisting of two competing neural networks—a generator and a discriminator—that jointly learn to produce increasingly realistic synthetic content. The generator creates synthetic samples; the discriminator attempts to distinguish them from real ones. This adversarial loop has become the dominant method for creating convincing deepfakes, synthetic images, and synthetic video.
Key architectural variants¶
Standard GANs: Two-player minimax game between generator and discriminator.
StyleGAN and StyleGAN2: High-quality face synthesis with intuitive control over style at different resolutions; enabled generation of photorealistic faces of non-existent people.
Progressive GANs (PGGAN): Growing network architecture during training to progressively increase resolution, improving stability and output quality.
Conditional GANs: Incorporate class labels or other conditioning information to guide generation (e.g., generating faces of specific identities).
Video GANs: Extensions for temporal coherence across video frames; critical for deepfake video synthesis.
Applications to deepfakes and synthetic media¶
- Face-swapping: Identity transfer using encoder-decoder GAN pairs
- Lip-sync synthesis: Decoupling identity from speech to create realistic mouth movements
- Full-face synthesis: Generating entirely synthetic but photorealistic faces
- Expression transfer: Mapping facial expressions from one person to another
Key papers in this wiki¶
- Deep Learning for Deepfakes Creation and Detection: A Survey — Comprehensive survey covering GAN-based creation methods, encoder-decoder architectures, and StyleGAN variants for deepfake synthesis; reviews creation tools and their capabilities
- The Creation and Detection of Deepfakes: A Survey — Systematic review of generative architectures including vanilla GANs, progressive GANs, and video-specific GAN variants
- Media Forensics and DeepFakes: an overview — Discussion of GAN-generated content and forensic detection approaches targeting GAN-specific artifacts
Related topics¶
- Deepfakes — Applications of GANs to facial video synthesis
- Synthetic media — Broader category including GAN-generated images and audio
- Facial manipulation — Face-specific manipulation techniques