扩散模型生成式图像检测技术研究综述

doi:10.3778/j.issn.1002-8331.2508-0156

摘要/Abstract

摘要： 扩散模型是一种通过前向扩散和反向去噪实现内容生成的模型。其在目标检测、医学图像、自然语言处理和生成式图像等领域得到了广泛的应用。随着应用范围扩大，鉴定生成图像的真实性成为了学术界研究的热点。但是，扩散模型生成式图像技术被用来制作虚假新闻图片或色情图片传播谣言等，其被广泛应用在灰色地带甚至违法犯罪领域。近年来，大量的研究工作用以解决扩散模型生成图像的真实性问题，然而，现有工作缺乏对其生成图像检测的系统性调研和梳理。为了填补上述空白，现对扩散模型生成式图像检测技术的研究发展进行了全面的分析和总结。概述了十种扩散模型生成图像技术的整体流程和相关步骤，研究扩散模型与其他的图像生成模型优缺点；系统性梳理出五类扩散模型检测技术，讨论了检测技术的应用和挑战，将五类检测技术对比分析；总结了二十二种扩散模型数据集，并将所有数据集进行系统性对比；根据扩散模型生成式图像检测技术的局限性，探讨了检测技术在今后的发展方向。

关键词: 扩散模型, 生成式图像检测, 深度学习

Abstract: Diffusion model is a model that realizes content generation through forward diffusion and backward denoising. It has been widely used in the fields of target detection, medical images, natural language processing and generative images. With the expansion of the application scope, characterizing the authenticity of the generated images has become a hotspot of academic research. However, the diffusion model generative image technology is used to produce false news images or pornographic images to spread rumors, etc., and it is widely used in the gray area and even in the field of illegal crimes. In recent years, a large number of research works have been used to solve the authenticity problem of diffusion model-generated images, but the existing works lack the systematic research and combing of its generated image detection. In order to fill the above gap, the research and development of diffusion model-generated image detection technology is comprehensively analyzed. This paper outlines the overall process and related steps of ten diffusion model-generated image technologies, and studies the advantages and disadvantages of diffusion model and other image generation models. It systematically sorts out five types of diffusion model detection technologies, discusses the applications and challenges of the detection technologies, and compares and analyzes the five types of detection technologies. It summarizes 22 types of diffusion model datasets, and makes a systematic comparison. According to the limitations of the diffusion model generative image detection technology, the future development direction of the detection technology is discussed.

Key words: diffusion model, generative image detection, deep learning

程泊宣, 李明轩, 张正宇. 扩散模型生成式图像检测技术研究综述[J]. 计算机工程与应用, 2025, 61(20): 1-18.

CHENG Boxuan, LI Mingxuan, ZHANG Zhengyu. Review of Generative Image Detection Technology Based on Diffusion[J]. Computer Engineering and Applications, 2025, 61(20): 1-18.

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