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，能够生成具有惊人逼真感的图像。这引发了对其可能被恶意利用的深切担忧。在本文中，我们提出利用轻量级的多局部内在维度（multiLID）方法——该方法最初开发用于对抗性样本检测——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（往往仅适用于GAN生成的图像）不同，所提方法在多种实际使用场景中实现了近乎完美的检测效果。针对已知及新构建数据集的广泛实验表明，所提multiLID方法在扩散检测及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出版物中，其实证评估通常主要聚焦于"LSUN-Bedroom"数据集，我们进一步构建了一个针对扩散生成图像的全面检测基准，其中包含来自多个不同图像尺寸扩散模型的样本。