哈尔滨商业大学学报（自然科学版）2026，Vol.42Issue(2)：147-154,8.
目标域增强驱动的无监督域自适应人脸伪造检测
Unsupervised domain adaptation for face forgery detection via target domain enhancement
摘要
Abstract
The generalization ability of current face forgery detection methods remained insufficient for deployment in real-world scenarios.To address this issue,this paper proposed an unsupervised domain adaptation(UDA)framework that enhanced detection performance on target domains with unseen forgery patterns.Specifically,considering the significant domain discrepancies introduced by different forgery generation methods,designed a cross-domain feature perturbation strategy that incorporated target domain features into the source domain,enabling indirect domain alignment.The adversarial learning module was applied to further align forged features from both domains.Additionally,introduced a discriminative clustering loss,which combined an entropy loss and a class-balancing loss.This loss simultaneously improved the model's transferability and ensured balanced classification.Extensive experiments on the DF,F2F,and FS datasets demonstrated that their approach consistently outperformed existing domain generalization and domain adaptation methods,achieving over 95%in both AUC and F1-scores on the target domains.关键词
无监督域自适应/人脸伪造检测/域差异/域对抗神经网络/熵损失/类别均衡损失Key words
unsupervised domain adaptation/face forgery detection/domain discrepancy/domain-adversarial neural network/entropy loss/class-balancing loss分类
信息技术与安全科学引用本文复制引用
孙标虎,杨高明..目标域增强驱动的无监督域自适应人脸伪造检测[J].哈尔滨商业大学学报（自然科学版）,2026,42(2):147-154,8.基金项目
国家自然科学基金资助项目(52374155) （52374155）
安徽省自然科学基金资助项目(2308085MF218) （2308085MF218）
