文献类型：期刊文献

英文题名：Wavelet-Based Texture Mining and Enhancement for Face Forgery Detection

作者：Li, Xin[1,2];Zhao, Hui[1,2];Xu, Bingxin[1,2];Liu, Hongzhe[1,2]

通讯作者：Xu, BX[1];Liu, HZ[1];Xu, BX[2];Liu, HZ[2]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2025

卷号：2025

期号：1

外文期刊名：IET BIOMETRICS

收录：;EI(收录号：20250917978700);Scopus(收录号：2-s2.0-85218924833);WOS:【SCI-EXPANDED(收录号:WOS:001419710000001)】；

基金：This work was supported by the National Natural Science Foundation of China (Nos. 62006020, 62171042, and 62102033).

语种：英文

外文关键词：face forgery detection; frequency-guided texture enhancement; image frequency analysis; multiscale feature interaction

摘要：Due to the abuse of deep forgery technology, the research on forgery detection methods has become increasingly urgent. The corresponding relationship between the frequency spectrum information and the spatial clues, which is often neglected by current methods, could be conducive to a more accurate and generalized forgery detection. Motivated by this inspiration, we propose a wavelet-based texture mining and enhancement framework for face forgery detection. First, we introduce a frequency-guided texture enhancement (FGTE) module that mining the high-frequency information to improve the network's extraction of effective texture features. Next, we propose a global-local feature refinement (GLFR) module to enhance the model's leverage of both global semantic features and local texture features. Moreover, the interactive fusion module (IFM) is designed to fully incorporate the enhanced texture clues with spatial features. The proposed method has been extensively evaluated on five public datasets, such as FaceForensics++ (FF++), deepfake (DF) detection (DFD) challenge (DFDC), Celeb-DFv2, DFDC preview (DFDC-P), and DFD, for face forgery detection, yielding promising performance within and cross dataset experiments.