基于多特征协同置信感知的深度伪造图像检测与定位方法

郭祯; 邱润尧; 徐嘉; 刘志全; 马建峰; 张文杰

doi:10.11959/j.issn.1000-436x.TXXB260151

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基于多特征协同置信感知的深度伪造图像检测与定位方法

学术论文 | 更新时间：2026-09-09

- 基于多特征协同置信感知的深度伪造图像检测与定位方法
- Deepfake image detection and localization method based on mul-ti-feature collaborative confidence awareness
- 通信学报 2026年47卷第8期页码：156-175
- 作者机构：
  
  1.海南大学网络空间安全学院（密码学院），海南海口 570228
  2.暨南大学网络空间安全学院，广东广州 510632
  3.西安电子科技大学网络与信息安全学院，陕西西安 710071
  4.慕尼黑工业大学工程与设计学院，慕尼黑 85521
- 作者简介：
  
  郭祯（1981- ），女，博士，海南大学副教授，主要研究方向为数据安全、隐私保护与区块链技术。
  邱润尧（2002- ），男，海南大学硕士生，主要研究方向为网络与信息安全、深度伪造检测。
  徐嘉（1997- ），女，海南大学博士生，主要研究方向为医学图像处理、数字水印、深度神经网络、计算机视觉等。
  刘志全（1989- ），男，博士，暨南大学教授，主要研究方向为车联网安全、低空安全、Web安全、信任建模、隐私计算、区块链、人工智能等。
  马建峰（1963- ），男，博士，西安电子科技大学教授，主要研究方向为网络安全、系统安全、数据安全、无人机安全等。
  张文杰（1999- ），男，西安电子科技大学博士生，德国慕尼黑工业大学联合培养，主要研究方向为复杂系统临界转变的深度学习预测、计算机视觉与遥感图像智能解译。
- 基金信息：
  
  国家自然科学基金资助项目(62562027)
- DOI：10.11959/j.issn.1000-436x.TXXB260151
  中图分类号： TP309
- 收稿：2026-03-26，
  
  修回：2026-05-21，
  
  录用：2026-05-22，
  
  纸质出版：2026-08-25
- 稿件说明：
移动端阅览
郭祯,邱润尧,徐嘉等.基于多特征协同置信感知的深度伪造图像检测与定位方法[J].通信学报,2026,47(08):156-175.

Guo Zhen,Qiu Runyao,Xu Jia,et al.Deepfake image detection and localization method based on mul-ti-feature collaborative confidence awareness[J].Journal on Communications,2026,47(08):156-175.
郭祯,邱润尧,徐嘉等.基于多特征协同置信感知的深度伪造图像检测与定位方法[J].通信学报,2026,47(08):156-175. DOI： 10.11959/j.issn.1000-436x.TXXB260151.

Guo Zhen,Qiu Runyao,Xu Jia,et al.Deepfake image detection and localization method based on mul-ti-feature collaborative confidence awareness[J].Journal on Communications,2026,47(08):156-175. DOI： 10.11959/j.issn.1000-436x.TXXB260151.

摘要

针对现有方法在复杂篡改场景下泛化能力不足、篡改区域边界定位不够精确的问题，提出了一种基于多特征协同置信感知的深度伪造图像检测与定位方法。该方法通过构建语义-取证双流交互框架，协同利用图像语义信息与伪造痕迹信息，提升对篡改区域的检测与定位能力。同时，引入小波域注意力模块强化高频篡改痕迹表征，并结合像素级门控融合与级联解码逐步细化篡改区域边界。训练阶段采用两阶段优化策略，通过任务适配器引入图像级检测与置信度估计，以缓解多任务优化干扰。实验结果表明，相较于现有代表性方法，所提方法的平均F1值和AUC值分别达到69.5%和90.7%，在复杂篡改场景下表现出较好的检测性能与泛化能力。

Abstract

To address the limited generalization and inaccurate boundary localization of existing methods in complex manipulation scenarios

a deepfake image detection and localization method based on multi-feature collaborative confidence awareness was proposed. The method adopted a semantic-forensic dual-branch interactive framework to jointly exploit image content information and forgery trace information

thereby improving the detection and localization of tampered regions. A wavelet-domain attention module was further introduced to enhance the representation of high-frequency tampering traces

while pixel-level gated fusion and cascaded decoding were used to progressively refine manipulated boundaries. During training

a two-stage optimization strategy was adopted

where a task adapter introduced image-level detection and confidence estimation to alleviate multi-task optimization interference. Experimental results show that

compared with representative competing methods

the proposed method attains an average F1 of 69.5% and an average AUC of 90.7%

respectively. It achieves strong detection performance and generalization ability in complex manipulation scenarios.

关键词

Keywords

references

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