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In consumer electronics, fraudulent schemes leveraging Deepfake technology are widespread, making it urgent to safeguard users' data privacy and security. However, many Deepfake detection methods based on Convolutional Neural Networks (CNNs) struggle to achieve satisfactory performance on mainstream datasets, especially with heavily compressed images. Observing that tampered images leave traces in the frequency domain, which are imperceptible to the naked eye but detectable through spectrum analysis, this study proposes a novel face forgery detection framework integrating spatial and frequency domain features. The framework introduces three innovative modules: the cross\u2010attention fusion module (CAFM), the guided attention module (GAM), and the multi\u2010scale feature fusion module (MSFFM), Specifically, CAFM combines spatial and frequency\u2010domain features through cross\u2010attention to enhance feature interaction. GAM generates attention maps to refine the integration of spatial and frequency features, while MSFFM fuses multi\u2010scale hierarchical features to capture both global and local tampering artifacts. These modules collectively improve the richness and discrimination of the extracted features, contributing to the overall detection performance. The proposed method demonstrates its effectiveness and superiority in forgery detection tasks, achieving a 3.9% average improvement in AUC compared to the state\u2010of\u2010the\u2010art method GocNet\u00a0[1] on FaceForensics++ (FF++) and WildDeepfake datasets. Extensive experiments further validate the effectiveness of our\u00a0approach.<\/jats:p>","DOI":"10.1049\/ipr2.70131","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:29:33Z","timestamp":1750202973000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Face Forgery Detection via Multi\u2010Scale and Multi\u2010Domain Features Fusion"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4784-8247","authenticated-orcid":false,"given":"Rongrong","family":"Gong","sequence":"first","affiliation":[{"name":"Department of Software Changsha Social Work College  Changsha China"}]},{"given":"Jiahao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Changsha University of Science and Technology  Changsha China"}]},{"given":"Dengyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Changsha University of Science and Technology  Changsha China"}]},{"given":"Arun Kumar","family":"Sangaiah","sequence":"additional","affiliation":[{"name":"International Graduate Institute of AI National Yunlin University of Science and Technology  Douliou Taiwan"}]},{"given":"Mohammed J. F.","family":"Alenazi","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, College of Computer and Information Sciences King Saud University  Riyadh Saudi Arabia"}]}],"member":"265","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119361"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"D.Cozzolino G.Poggi andL.Verdoliva \u201cRecasting Residual\u2010Based Local Descriptors as Convolutional Neural Networks: An Application to Image Forgery Detection \u201d inProceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security(ACM 2017) 159\u2013164.","DOI":"10.1145\/3082031.3083247"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"F.Chollet \u201cXception: Deep Learning With Depthwise Separable Convolutions \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(IEEE 2017) 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","unstructured":"G.Huang Z.Liu L. 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