网络安全与数据治理2026，Vol.45Issue(8)：45-54,10.DOI:10.19358/j.issn.2097-1788.2026.08.006
融合注意力与图卷积的嵌套U型图像鉴伪网络
Nested U-shaped image forgery detection network fusing attention and graph convolution
摘要
Abstract
Image tampering poses a serious threat to digital media security,creating an urgent need for more robust detection methods.Exist-ing forgery detection approaches often struggle to effectively capture fine-grained tampering features and fuse multi-scale contextual information.To address these issues,we adapt a nested U-shaped network from the saliency detection domain to the image forensics task.In view of the subtle differences between tampered and authentic regions,we propose,through systematic experiments,a detection network that combines multi-scale attention with graph convolution.Experimental results on four benchmark datasets demonstrate that the proposed method achieves superior performance compared to current mainstream deep learning approaches.关键词
图像篡改定位/U2-Net/多尺度注意力/图卷积Key words
image tampering localization/U2-Net/multi-scale attention/graph convolution分类
信息技术与安全科学引用本文复制引用
熊思宇,沈姿含,吴柯非,江非宇,李琳,李青岩..融合注意力与图卷积的嵌套U型图像鉴伪网络[J].网络安全与数据治理,2026,45(8):45-54,10.基金项目
福建省科技厅创新战略研究项目(2024R0046) （2024R0046）
