Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification. Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.

翻译：扩散模型近期在视觉合成领域成功生成了极为逼真的图像，这引发了对它们被恶意利用的严重担忧。本文提出采用最初为检测对抗样本而开发的轻量级多局部本征维度（multiLID）方法，用于自动检测合成图像并识别相应的生成器网络。与许多仅能有效检测GAN生成图像的现有方法不同，所提方法在多个实际应用场景中实现了近乎完美的检测结果。在已知数据集和新构建数据集上的大量实验表明，multiLID方法在扩散检测和模型识别方面展现出优越性。鉴于近期关于生成图像检测的实证评估多聚焦于"LSUN-Bedroom"数据集，我们进一步建立了涵盖多种尺寸不同的扩散模型样本的综合检测基准。