With advancements in AI-generated images coming on a continuous basis, it is increasingly difficult to distinguish traditionally-sourced images (e.g., photos, artwork) from AI-generated ones. Previous detection methods study the generalization from a single generator to another in isolation. However, in reality, new generators are released on a streaming basis. We study generalization in this setting, training on N models and testing on the next (N+k), following the historical release dates of well-known generation methods. Furthermore, images increasingly consist of both real and generated components, for example through image inpainting. Thus, we extend this approach to pixel prediction, demonstrating strong performance using automatically-generated inpainted data. In addition, for settings where commercial models are not publicly available for automatic data generation, we evaluate if pixel detectors can be trained solely on whole synthetic images.

翻译：随着AI生成图像的持续进步，区分传统来源图像（例如照片、艺术作品）与AI生成图像日益困难。以往的检测方法孤立地研究从单一生成器到另一生成器的泛化能力。然而在现实中，新生成器以流式方式持续发布。我们研究了此场景下的泛化能力：根据知名生成方法的历史发布日期，使用N个模型进行训练，并在第（N+k）个模型上进行测试。此外，图像越来越多地包含真实与生成组件——例如通过图像修复技术。因此，我们将该方法扩展至像素级预测，利用自动生成的修复数据展现出强大性能。另外，针对商业模型未公开提供自动数据生成的场景，我们评估了仅使用完整合成图像训练像素级检测器的可行性。