Generative models have reached an advanced stage where they can produce remarkably realistic images. However, this remarkable generative capability also introduces the risk of disseminating false or misleading information. Notably, existing image detectors for generated images encounter challenges such as low accuracy and limited generalization. This paper seeks to address this issue by seeking a representation with strong generalization capabilities to enhance the detection of generated images. Our investigation has revealed that real and generated images display distinct latent Gaussian representations when subjected to an inverse diffusion process within a pre-trained diffusion model. Exploiting this disparity, we can amplify subtle artifacts in generated images. Building upon this insight, we introduce a novel image representation known as Diffusion Noise Feature (DNF). DNF is an ensemble representation that estimates the noise generated during the inverse diffusion process. A simple classifier, e.g., ResNet, trained on DNF achieves high accuracy, robustness, and generalization capabilities for detecting generated images, even from previously unseen classes or models. We conducted experiments using a widely recognized and standard dataset, achieving state-of-the-art effects of Detection.

翻译：生成模型已发展到能够生成极其逼真图像的高级阶段。然而，这种卓越的生成能力也带来了传播虚假或误导性信息的风险。值得注意的是，现有用于检测生成图像的检测器面临准确率低和泛化能力有限等挑战。本文旨在通过寻求一种具有强泛化能力的表示方法来增强对生成图像的检测。我们的研究表明，在预训练扩散模型中的逆扩散过程作用下，真实图像和生成图像表现出不同的潜在高斯表示。利用这种差异，我们可以放大生成图像中的细微伪影。基于这一发现，我们引入了一种新颖的图像表示方法——扩散噪声特征（Diffusion Noise Feature, DNF）。DNF是一种集成表示，用于估计逆扩散过程中生成的噪声。基于DNF训练的简单分类器（例如ResNet）在检测生成图像时，即使面对以前未见过的类别或模型，也能实现高准确率、鲁棒性和泛化能力。我们使用广泛认可的标准数据集进行了实验，达到了检测任务的最新效果。