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.

生成模型能够产生逼真的图像，但也可能传播虚假或误导性信息。本文通过寻求具有强大泛化能力的表示形式来增强对生成图像的检测，并提出了一种名为 Diffusion Noise Feature（DNF）的图像表示方法，利用潜在的高斯表示差异提升生成图像中的微小伪迹。在 DNF 上训练的简单分类器（如 ResNet）能够实现高准确性、鲁棒性和泛化能力，即使对于之前未见的类别或模型，也能检测生成图像。通过在广泛认可的标准数据集上进行实验，获得了检测效果的最新成果。