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.

基于人工智能图像生成的技术不断提升，产生更具逼真性且视觉缺陷较少的图像。这些图像被用于创建虚假在线账户，用于垃圾邮件、欺诈和虚假信息传播活动。本研究聚焦于区分真实的面部和基于人工智能生成的面部，以解决使用伪造用户头像的非真实在线账户检测问题。通过仅关注面部，我们展示了一种更具有弹性和通用性的工件，可以检测来自各种生成对抗网络和扩散基础合成引擎的人工智能生成面部，同时适用于多种图像分辨率（低至 128 x 128 像素）和质量。