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：扩散模型近期成功应用于视觉合成，能够生成极具真实感的图像，这引发了对其可能被恶意利用的严重担忧。本文提出采用轻量级的多重局部本征维度（multiLID）方法——该方法最初为检测对抗样本而开发——用于自动检测合成图像并识别相应的生成器网络。与许多仅适用于GAN生成图像的现有检测方法不同，所提出的方法在多种实际场景中均能实现近乎完美的检测效果。基于已知及新创建数据集的大量实验表明，multiLID方法在扩散检测和模型识别方面表现出优越性。鉴于近期关于生成图像检测的公开文献中的实证评估主要集中于LSUN-Bedroom数据集，我们进一步建立了一个针对扩散生成图像检测的综合性基准，包含来自多种不同图像尺寸的扩散模型样本。