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重新审视DIRE:朝着通用的人工智能生成图像检测的方向

Huanqi Lin1, Jinghui Qin1, Xiaoqi Wu1

  • 1School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China.

Neural networks : the official journal of the International Neural Network Society
|September 13, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了通用重建残余分析 (UR2EA) 来检测合成图像. 该方法分析重建错误,以区分真实图像与GAN和扩散模型产生的图像,实现最先进的检测精度.

关键词:
生成型模型是一种生成型模型.多尺度通道和窗口的注意力.重建的残余错误重建的残余错误合成图像检测检测器

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 数字法医学数字法医学

背景情况:

  • 像GAN和扩散模型这样的生成模型已经迅速发展,改善了图像合成质量和可访问性.
  • 这种进步引发了关于数字内容的可信性和真实性的重大担忧.
  • 现有的方法难以可靠地检测复杂的合成图像.

研究的目的:

  • 提出一种新的方法来检测各种生成模型产生的合成图像.
  • 建立一种普遍的方法来识别图像操纵和深度假冒.
  • 为了提高合成图像检测的准确性和可靠性.

主要方法:

  • 开发了使用预训练的扩散模型的重建错误进行通用重建残余分析 (UR2EA).
  • 引入了多尺度通道和窗口注意 (MCWA) 模块,用于从剩余地图中提取细粒度特征.
  • 构建了UniversalForensics数据集,使用来自30个不同的生成模型的多样化合成图像.

主要成果:

  • 基于重建错误模式,UR2EA有效地区分真实,GAN生成和扩散生成的图像.
  • 通过GAN生成的图像具有较低的重建质量,而通过扩散生成的图像与真实图像相比显示出更高的保真度.
  • 提出的方法取得了最先进的结果,比现有的基线平均精度提高了3.3%,精度提高了1.6%.

结论:

  • UR2EA提供了一个强大的和通用的先验,用于检测不同生成方法的合成图像.
  • MCWA模块增强了从剩余地图中提取特征,捕获关键的本地和全球细节.
  • 环球法学数据集促进了合成图像检测的进一步研究和开发.