Abstract
The rapid advancement of generative AI has raised concerns about the authenticity of digital images, as highly realistic fake images can now be generated at low cost, potentially increasing societal risks. In response, several datasets have been established to train detection models aimed at distinguishing AI-generated images from real ones. However, existing datasets suffer from limited generalization, low image quality, overly simple prompts, and insufficient image diversity. To address these limitations, we propose a high-quality, large-scale dataset comprising over 730,000 images across multiple categories, including both real and AI-generated images. The generated images are synthesized via state-of-the-art methods, including text-to-image generation (guided by over 10,000 carefully designed prompts), image inpainting, image refinement, and face swapping. Each generated image is annotated with its generation method and category. Inpainting images further include binary masks to indicate inpainted regions, providing rich metadata for analysis. Compared to existing datasets, detection models trained on our dataset demonstrate superior generalization capabilities. Our dataset not only serves as a strong benchmark for evaluating detection methods but also contributes to advancing the robustness of AI-generated image detection techniques. Building upon this, we propose a lightweight detection method based on image noise entropy, which transforms the original image into an entropy tensor of Non-Local Means (NLM) noise before classification. Extensive experiments demonstrate that models trained on our dataset achieve strong generalization, and our method delivers competitive performance, establishing a solid baseline for future research. The dataset and source code are publicly available at this https URL.
Abstract (translated)
生成式人工智能的快速发展引发了人们对数字图像真实性问题的关注，因为现在可以低成本地生成极其逼真的假图片，这可能增加社会风险。为应对这一挑战，已建立了几个数据集来训练检测模型以区分AI生成的图像和真实图像。然而，现有的数据集存在泛化能力有限、图像质量低、提示过于简单以及缺乏图像多样性等问题。为了克服这些限制，我们提出了一项高质量的大规模数据集，该数据集包含超过73万张跨多个类别的图像，包括真实的和AI生成的图像。所生成的图像是通过最先进的方法合成的，包括文本到图像生成（由10,000多个精心设计的提示指导）、图像修复、图像改进和面部替换。每一张生成的图像都标注了其生成的方法和类别。在经过修复处理的图片中还包括二进制掩模以指示修复区域，这为分析提供了丰富的元数据。 与现有数据集相比，在我们的数据集上训练的检测模型展示了更出色的泛化能力。除了作为一个强大的基准来评估检测方法外，该数据集还促进了AI生成图像检测技术的鲁棒性的进步。基于此，我们提出了一种轻量级的检测方法，它通过将原始图片转换为非局部均值（NLM）噪声的熵张量来进行分类前处理。广泛的实验表明，在我们的数据集上训练的模型能够实现强大的泛化能力，并且我们的方法提供了具有竞争力的表现，从而为未来的研究奠定了坚实的基础。 该数据集和源代码可在以下网址公开获取：[此URL]
URL
https://arxiv.org/abs/2602.10546
