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：扩散模型最近被成功应用于视觉合成，能够生成逼真度惊人的图像。这引发了人们对其被用于恶意目的的强烈担忧。本文提出使用轻量级的多局部固有维数（multiLID）方法——该方法最初是为检测对抗性样本而开发的——来自动检测合成图像并识别相应的生成器网络。与许多现有检测方法（通常仅适用于GAN生成的图像）不同，所提出的方法在许多实际场景中能够提供近乎完美的检测结果。在已知和新创建数据集上的大量实验表明，所提出的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出版物中经验评估主要聚焦于"LSUN-Bedroom"数据集，我们进一步建立了一个用于检测扩散生成图像的综合性基准测试，其中包括来自多个不同图像尺寸的扩散模型的样本。