Diffusion models (DMs) have recently emerged as a promising method in image synthesis. However, to date, only little attention has been paid to the detection of DM-generated images, which is critical to prevent adverse impacts on our society. In this work, we address this pressing challenge from two different angles: First, we evaluate the performance of state-of-the-art detectors, which are very effective against images generated by generative adversarial networks (GANs), on a variety of DMs. Second, we analyze DM-generated images in the frequency domain and study different factors that influence the spectral properties of these images. Most importantly, we demonstrate that GANs and DMs produce images with different characteristics, which requires adaptation of existing classifiers to ensure reliable detection. We are convinced that this work provides the foundation and starting point for further research on effective detection of DM-generated images.

翻译：扩散模型（DMs）近期已成为图像合成领域的一种有前景的方法。然而，目前对于扩散模型生成图像的检测研究甚少，而这对防止其对社会造成不利影响至关重要。在本工作中，我们从两个不同角度应对这一紧迫挑战：首先，我们评估了当前最先进的检测器（这些检测器对生成对抗网络（GANs）生成的图像非常有效）在一系列扩散模型上的性能。其次，我们在频域中分析了扩散模型生成的图像，并研究了影响这些图像频谱特性的不同因素。最重要的是，我们证明了GANs和扩散模型生成的图像具有不同特征，这需要对现有分类器进行适配以确保可靠检测。我们确信，本工作为后续有效检测扩散模型生成图像的研究奠定了基础并提供了起点。