Finding AI-Generated Faces in the Wild

TL;DR

This paper presents a method for detecting AI-generated faces in online profiles by focusing on facial artifacts, achieving robustness across various synthesis engines, resolutions, and qualities.

Contribution

The study introduces a face-focused detection approach that effectively identifies AI-generated faces from multiple synthesis methods and image qualities, enhancing online authenticity verification.

Findings

Effective detection across GAN and diffusion models

Robust performance at low resolutions (128x128)

Applicable to real-world online profile scenarios

Abstract

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.