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：扩散模型近期已成功应用于视觉合成，生成逼真度惊人的图像。这引发了人们对这些模型可能被用于恶意目的的严重担忧。本文提出使用轻量级的多重局部本征维数（multiLID）方法——该方法最初为检测对抗性样本而开发——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（通常仅对GAN生成图像有效）不同，所提方法在众多实际应用场景中能实现近乎完美的检测效果。在已知和新创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具有优越性。鉴于近期关于生成图像检测的实证评估主要集中于"LSUN-Bedroom"数据集，我们进一步建立了包含多个扩散模型不同尺寸样本的扩散生成图像检测综合基准。