Recent generative models show impressive performance in generating photographic images. Humans can hardly distinguish such incredibly realistic-looking AI-generated images from real ones. AI-generated images may lead to ubiquitous disinformation dissemination. Therefore, it is of utmost urgency to develop a detector to identify AI generated images. Most existing detectors suffer from sharp performance drops over unseen generative models. In this paper, we propose a novel AI-generated image detector capable of identifying fake images created by a wide range of generative models. We observe that the texture patches of images tend to reveal more traces left by generative models compared to the global semantic information of the images. A novel Smash&Reconstruction preprocessing is proposed to erase the global semantic information and enhance texture patches. Furthermore, pixels in rich texture regions exhibit more significant fluctuations than those in poor texture regions. Synthesizing realistic rich texture regions proves to be more challenging for existing generative models. Based on this principle, we leverage the inter-pixel correlation contrast between rich and poor texture regions within an image to further boost the detection performance. In addition, we build a comprehensive AI-generated image detection benchmark, which includes 17 kinds of prevalent generative models, to evaluate the effectiveness of existing baselines and our approach. Our benchmark provides a leaderboard for follow-up studies. Extensive experimental results show that our approach outperforms state-of-the-art baselines by a significant margin. Our project: https://fdmas.github.io/AIGCDetect

翻译：近期生成模型在生成摄影级图像方面展现出卓越性能。人类难以区分此类逼真到难以置信的AI生成图像与真实图像。AI生成图像可能导致虚假信息的泛化传播，因此开发识别AI生成图像的检测器具有极高紧迫性。现有检测器在面对未见过的生成模型时普遍存在性能急剧下降的问题。本文提出一种新型AI生成图像检测器，能够识别由多种生成模型创建的虚假图像。我们观察到相较于图像的全局语义信息，纹理块更倾向于暴露生成模型留下的痕迹。为此提出创新的破坏-重建预处理方法，通过消除全局语义信息并增强纹理块特征。进一步研究发现，丰富纹理区域的像素波动显著大于贫瘠纹理区域，现有生成模型合成逼真的丰富纹理区域更具挑战性。基于这一原理，我们利用图像内部丰富与贫瘠纹理区域的像素间相关性对比来提升检测性能。此外，我们构建了包含17种主流生成模型的综合性AI生成图像检测基准，用于评估现有基线方法及本文方法的有效性，该基准为后续研究提供排行榜。大量实验结果表明，我们的方法以显著优势超越当前最先进的基线方法。项目主页：https://fdmas.github.io/AIGCDetect