湖北民族大学学报（自然科学版）2026，Vol.44Issue(1)：69-74,6.DOI:10.13501/j.cnki.42-1908/n.2026.03.005
基于Decoupled-FR Net的伪造图像检测模型
Forged Image Detection Model Based on Decoupled-FR Net
摘要
Abstract
To address the challenges of fine-grained feature perception and cross-region dependency modeling in forged image detection,a detection model named decoupled frequency refinement network(Decoupled-FR Net)was proposed.The model was designed with a spatial-channel decoupled attention mechanism,which effectively avoided the feature coupling problem in traditional hybrid attention and enhanced the independence and discriminative power of feature representation.A feature refinement module was introduced to enhance the perception ability of subtle tampering through hierarchical feature calibration and fusion.Combining with a context-aware mechanism,long-distance dependencies across regions were captured,thereby improving the overall detection performance.The results showed that,the accuracy and average precision of the Decoupled-FR Net model on the forensic synthetics(ForenSynths)dataset were improved by 2.4 and 0.5 percentage points,respectively,compared with the inter-patch dependency network(IPD-Net)model,and on the generative adversarial network(GAN)generation detection(GANGen-Detection)dataset,average precision was improved by 0.1 percentage points,compared with the frequency domain network(FreqNet)model.The model provided a new solution for fine-grained forged image detection and was of important application value in the field of multimedia forensics.关键词
空间-通道注意力解耦/特征细化/频域增强/上下文感知/跨模型伪造图像检测/生成式对抗网络Key words
spatial-channel attention decoupling/feature refinement/frequency domain enhancement/context awareness/cross-model forged image detection/generative adversarial network分类
信息技术与安全科学引用本文复制引用
杨桃,张乾,文露露,彭杉..基于Decoupled-FR Net的伪造图像检测模型[J].湖北民族大学学报（自然科学版）,2026,44(1):69-74,6.基金项目
贵州省教育厅自然科学研究项目(黔教技[2023]012) （黔教技[2023]012）
贵州民族大学校级科研项目(GZMUZK[2021]YB23,GZMUZK[2023]QN10). （GZMUZK[2021]YB23,GZMUZK[2023]QN10）
