融合色彩空间特征与频域特征的多源伪造图像检测方法

侯壹凡; 苗壮; 王俊; 张奇; 李阳; 王家宝

doi:10.11959/j.issn.2096-109x.2026011

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融合色彩空间特征与频域特征的多源伪造图像检测方法

学术论文 | 更新时间：2026-05-26

- 融合色彩空间特征与频域特征的多源伪造图像检测方法
- Multi-source forgery detection method based on the fusion of color space features and frequency domain features
- 网络与信息安全学报 2026年12卷第1期页码：141-150
- 作者机构：
  
  陆军工程大学指挥控制工程学院，江苏南京 210000
- 作者简介：
  
  侯壹凡（1994- ），男，河南鹤壁人，陆军工程大学博士生，主要研究方向为计算机视觉。
  苗壮（1976- ），男，辽宁辽阳人，陆军工程大学教授，主要研究方向为图像视频处理。
  王俊（1997- ），男，江苏盐城人，陆军工程大学硕士生，主要研究方向为智能安全。
  张奇（2000- ），男，江苏徐州人，陆军工程大学硕士生，主要研究方向为图像复原。
  李阳（1984- ），男，河北廊坊人，陆军工程大学副教授，主要研究方向为计算机视觉、图像检索
  王家宝（1985- ），男，安徽合肥人，陆军工程大学副教授，主要研究方向为计算机视觉、图像处理。
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2026011
  中图分类号： TP393
- 收稿：2025-10-20，
  
  修回：2025-12-29，
  
  纸质出版：2026-02-25
- 稿件说明：
移动端阅览
侯壹凡, 苗壮, 王俊, 等. 融合色彩空间特征与频域特征的多源伪造图像检测方法[J]. 网络与信息安全学报, 2026, 12(1): 141-150.

Hou Yifan, Miao Zhuang, Wang Jun, et al. Multi-source forgery detection method based on the fusion of color space features and frequency domain features[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 141-150.
侯壹凡, 苗壮, 王俊, 等. 融合色彩空间特征与频域特征的多源伪造图像检测方法[J]. 网络与信息安全学报, 2026, 12(1): 141-150. DOI： 10.11959/j.issn.2096-109x.2026011.

Hou Yifan, Miao Zhuang, Wang Jun, et al. Multi-source forgery detection method based on the fusion of color space features and frequency domain features[J]. Chinese Journal of Network and Information Security, 2026, 12(1): 141-150. DOI： 10.11959/j.issn.2096-109x.2026011.

摘要

随着人工智能技术的迅猛发展，图像伪造手段变得日益复杂和隐蔽，尤其是生成式伪造和图像编辑伪造手段，使传统检测方法的泛化能力不足问题逐渐凸显。由于不同的伪造方式生成的图像存在显著差异，现有研究往往局限于仅针对单一的伪造类型，缺乏面向多源伪造的统一检测方法。为应对生成式伪造和图像编辑伪造带来的多源挑战，提出了一种融合色彩空间特征与频域特征的多源伪造图像检测方法，旨在提高当前图像伪造检测技术在复杂伪造场景下的泛化能力和鲁棒性。该方法从图像中提取色彩空间伪影特征、频域高频细节痕迹和语义信息，通过构建的多域特征融合模块有效捕捉伪造图像中的光影异常与伪影，实现对生成式伪造和编辑式伪造的多源伪造内容的准确识别。此外，还构建了一个涵盖生成式伪造和编辑式伪造的多源伪造图像数据集，更真实地模拟了现实中可能出现的伪造图像场景。实验结果表明，所提方法在多源伪造图像检测任务中的性能显著优于现有方法，尤其是在复杂伪造场景下表现出更强的适应性和准确性。

Abstract

With the rapid development of artificial intelligence technology

image forgery techniques have become increasingly complex and covert

particularly in the areas of generative and image editing forgeries. Traditional detection methods have gradually revealed their limitations and shortcomings in dealing with these advanced forms of forgery. Due to significant differences in images generated by various types of forgeries

current research is often limited to detecting specific forgery types

with a lack of unified detection methods. To address the multi-source challenge posed by generative and image editing forgeries

a multi-source forgery image detection method that inte-grates color space features with frequency domain features was proposed . The proposed method aims to enhance the generalization ability and robustness of existing forgery detection techniques in complex forgery scenarios. This method extracts color space artifact features

frequency domain high-frequency detail traces and semantic information from the image. Through the constructed multi-domain feature fusion module

it effectively captures the abnormal light and shadow and artifacts in the forged image

achieving accurate identification of multi-source forged content including generative forgery and editing-based forgery. In addition

a multi-source forgery image dataset was constructed that includes both generative and editing forgeries

offering a more realistic representation of potential forgery scenarios in real-world applications. Experimental results demonstrate that the proposed method significantly outperforms existing methods in the detection of multi-source forgery images

especially in complex forgery environments

showing superior adaptability and accuracy.

关键词

Keywords

references

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