湖南大学学报（自然科学版）2025，Vol.52Issue(4)：27-33,7.DOI:10.16339/j.cnki.hdxbzkb.2025263
结合注意力机制和Gabor滤波器的人脸伪造检测
Face Forgery Detection Combining Attention Mechanism and Gabor Filter
摘要
Abstract
In view of the significant texture difference between fake faces and real faces,this paper proposes a face forgery detection model based on texture features.Firstly,ResNet18 is used as the backbone network,and combined with the channel attention mechanism and residual network to solve the problem of network degradation,in order to establish the connection between channels to extract deep features.Secondly,the autocorrelation matrix is used to quantify the correlation between image blocks,and the features of different scales in the image are captured to obtain global statistical features.Finally,the Gabor filter is introduced after each pooling layer of the autocorrelation module to extract the local texture features of the image,providing a comprehensive description of the image content,and the Softmax function is used to perform hierarchical classification.Experimental results show that this method effectively improves the detection accuracy for fake images edited by different image enhancement methods.关键词
人脸伪造检测/残差网络/注意力机制/自相关矩阵/Gabor滤波器Key words
face forgery detection/residual network/attention mechanism/autocorrelation matrix/Gabor filter分类
信息技术与安全科学引用本文复制引用
罗维薇,岳田田,雷琴..结合注意力机制和Gabor滤波器的人脸伪造检测[J].湖南大学学报（自然科学版）,2025,52(4):27-33,7.基金项目
国家自然科学基金资助项目(62362047),National Natural Science Foundation of China(62362047) （62362047）
