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。

扩散噪声特征：准确快速的生成图像检测

Diffusion Noise Feature: Accurate and Fast Generated Image Detection

The extraordinary ability of generative models to generate photographic
images has intensified concerns about the spread of disinformation, thereby
leading to the demand for detectors capable of distinguishing between
AI-generated fake images and real images. However, the lack of large datasets
containing images from the most advanced image generators poses an obstacle to
the development of such detectors. In this paper, we introduce the GenImage
dataset, which has the following advantages: 1) Plenty of Images, including
over one million pairs of AI-generated fake images and collected real images.
2) Rich Image Content, encompassing a broad range of image classes. 3)
State-of-the-art Generators, synthesizing images with advanced diffusion models
and GANs. The aforementioned advantages allow the detectors trained on GenImage
to undergo a thorough evaluation and demonstrate strong applicability to
diverse images. We conduct a comprehensive analysis of the dataset and propose
two tasks for evaluating the detection method in resembling real-world
scenarios. The cross-generator image classification task measures the
performance of a detector trained on one generator when tested on the others.
The degraded image classification task assesses the capability of the detectors
in handling degraded images such as low-resolution, blurred, and compressed
images. With the GenImage dataset, researchers can effectively expedite the
development and evaluation of superior AI-generated image detectors in
comparison to prevailing methodologies.

该论文介绍了 GenImage 数据集，该数据集拥有超过 100 万对人工智能生成的假图像和真实图像，且具有丰富的图像内容和最先进的生成器，通过该数据集，研究人员可以有效加快相对于现有方法的优越人工智能生成图像检测器的开发和评估。