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.

翻译：基于人工智能的图像生成技术持续快速进步，生成的图像越来越逼真，明显视觉缺陷越来越少。AI生成的图像正被用于创建虚假在线个人资料，进而用于垃圾邮件、欺诈和虚假信息宣传活动。随着检测任何类型篡改或合成内容的通用问题日益受到关注，我们聚焦于区分真实人脸与AI生成人脸这一更具体任务。这在处理使用虚假头像的非真实在线账户时尤其适用。研究表明，通过专注人脸区域，可以检测到更具韧性和通用性的伪影，从而能够识别来自多种GAN和扩散基合成引擎生成的AI人脸，且适用于低至128×128像素的不同分辨率和图像质量。