Diffusion models (DMs) are generative models that learn to synthesize images from Gaussian noise. DMs can be trained to do a variety of tasks such as image generation and image super-resolution. Researchers have made significant improvement in the capability of synthesizing photorealistic images in the past few years. These successes also hasten the need to address the potential misuse of synthesized images. In this paper, we highlight the effectiveness of computing local statistics, as opposed to global statistics, in distinguishing digital camera images from DM-generated images. We hypothesized that local statistics should be used to address the spatial non-stationarity problem in images. We show that our approach produced promising results and it is also robust to various perturbations such as image resizing and JPEG compression.

翻译：扩散模型（DMs）是一类从高斯噪声中学习合成图像的无监督生成模型。该模型可被训练用于执行图像生成、超分辨率重建等多种任务。近年来，研究者们在合成逼真图像的能力方面取得了显著进展，这些成功也加剧了应对合成图像潜在滥用问题的紧迫性。本文重点论证了相较于全局统计方法，局部统计在区分数码相机图像与扩散模型生成图像方面的有效性。我们提出假设：应当采用局部统计来解决图像中存在的空间非平稳性问题。实验表明，该方法不仅取得了令人满意的结果，还对图像缩放、JPEG压缩等多种扰动具有鲁棒性。