Generative artificial intelligence holds significant potential for abuse, and generative image detection has become a key focus of research. However, existing methods primarily focused on detecting a specific generative model and emphasizing the localization of synthetic regions, while neglecting the interference caused by image size and style on model learning. Our goal is to reach a fundamental conclusion: Is the image real or generated? To this end, we propose a diffusion model-based generative image detection framework termed Hierarchical Retrospection Refinement~(HRR). It designs a multi-scale style retrospection module that encourages the model to generate detailed and realistic multi-scale representations, while alleviating the learning biases introduced by dataset styles and generative models. Additionally, based on the principle of correntropy sparse additive machine, a feature refinement module is designed to reduce the impact of redundant features on learning and capture the intrinsic structure and patterns of the data, thereby improving the model's generalization ability. Extensive experiments demonstrate the HRR framework consistently delivers significant performance improvements, outperforming state-of-the-art methods in generated image detection task.

翻译：生成式人工智能具有显著的滥用潜力，生成图像检测已成为研究的关键焦点。然而，现有方法主要集中于检测特定的生成模型并强调合成区域的定位，而忽视了图像尺寸和风格对模型学习造成的干扰。我们的目标是得出一个根本性结论：图像是真实的还是生成的？为此，我们提出了一种基于扩散模型的生成图像检测框架，称为分层回顾细化（HRR）。它设计了一个多尺度风格回顾模块，鼓励模型生成详细且真实的多尺度表示，同时减轻由数据集风格和生成模型引入的学习偏差。此外，基于相关熵稀疏加性机原理，设计了一个特征细化模块，以减少冗余特征对学习的影响，并捕获数据的内在结构和模式，从而提高模型的泛化能力。大量实验表明，HRR框架始终能带来显著的性能提升，在生成图像检测任务中优于现有最先进方法。