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

作　　者：罗维薇[1] 岳田田 雷琴[1] LUO Weiwei;YUE Tiantian;LEI Qin(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,甘肃兰州730070

出　　处：《湖南大学学报(自然科学版)》2025年第4期27-33,共7页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(62362047)。

摘　　要：针对假人脸和真实人脸纹理的显著差异,提出了一种基于纹理特征的人脸伪造检测模型.首先,以ResNet18为主干网络,结合通道注意力机制和残差网络解决网络退化的问题,并建立通道之间的联系以提取深层特征;其次,运用自相关矩阵来量化图像块之间的相关性,捕捉图像中不同尺度的特征以获取全局统计特征;最后,通过在自相关模块的每个池化层后引入Gabor滤波器,提取图像的局部纹理特征,全面描述图像内容,并采用Softmax函数对输入图像进行层次化分类.实验结果表明,对于不同的图像增强方法编辑的假图像,该方法有效提升了检测准确率.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 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]