Finding AI-Generated Faces in the Wild
This addresses the issue of spam, fraud, and disinformation campaigns using AI-generated faces for fake online accounts, though it is incremental as it focuses on a narrow task within broader content manipulation detection.
The paper tackled the problem of distinguishing real faces from AI-generated ones, particularly for detecting fake online profiles, and demonstrated a method that detects faces from various synthesis engines across resolutions and qualities.
AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 x 128 pixels) and qualities.