Synthetic Face Generation

Synthetic face generation uses machine learning, primarily diffusion models and GANs, to create realistic artificial faces. Current research focuses on improving the discriminative quality of generated faces for applications like face recognition, developing methods to detect and attribute synthetic images to their generating algorithms (even in open-set scenarios), and addressing ethical concerns by generating datasets for under-represented conditions while mitigating privacy risks. This field is significant due to its implications for security (detecting deepfakes), biomedical research (creating diverse datasets for training medical AI), and the broader societal impact of increasingly realistic synthetic media.