{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:31:11Z","timestamp":1760059871886,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Gansu Province Higher Education Institutions Industrial Support Program","award":["2020C-29","6156200"],"award-info":[{"award-number":["2020C-29","6156200"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020C-29","6156200"],"award-info":[{"award-number":["2020C-29","6156200"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The rapid development of deepfake technologies has led to the widespread proliferation of facial image forgeries, raising significant concerns over identity theft and the spread of misinformation. Although recent dual-domain detection approaches that integrate spatial and frequency features have achieved noticeable progress, they still suffer from limited sensitivity to local forgery regions and inadequate interaction between spatial and frequency information in practical applications. To address these challenges, we propose a novel forgery-aware guided spatial\u2013frequency feature fusion network. A lightweight U-Net is employed to generate pixel-level saliency maps by leveraging structural symmetry and semantic consistency, without relying on ground-truth masks. These maps dynamically guide the fusion of spatial features (from an improved Swin Transformer) and frequency features (via Haar wavelet transforms). Cross-domain attention, channel recalibration, and spatial gating are introduced to enhance feature complementarity and regional discrimination. Extensive experiments conducted on two benchmark face forgery datasets, FaceForensics++ and Celeb-DFv2, show that the proposed method consistently outperforms existing state-of-the-art techniques in terms of detection accuracy and generalization capability. The future work includes improving robustness under compression, incorporating temporal cues, extending to multimodal scenarios, and evaluating model efficiency for real-world deployment.<\/jats:p>","DOI":"10.3390\/sym17071148","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T10:10:38Z","timestamp":1752833438000},"page":"1148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Forgery-Aware Guided Spatial\u2013Frequency Feature Fusion for Face Image Forgery Detection"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8441-9534","authenticated-orcid":false,"given":"Zhenxiang","family":"He","sequence":"first","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, No. 6 Anning West Road, Lanzhou 730070, China"}]},{"given":"Zhihao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, No. 6 Anning West Road, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6068-4499","authenticated-orcid":false,"given":"Ziqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Gansu University of Political Science and Law, No. 6 Anning West Road, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123732","DOI":"10.1016\/j.eswa.2024.123732","article-title":"DeepFake detection based on high-frequency enhancement network for highly compressed content","volume":"249","author":"Gao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s00371-021-02347-4","article-title":"A detailed analysis of image and video forgery detection techniques","volume":"39","author":"Tyagi","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dou, L., Feng, G., and Qian, Z. (2023). Image Inpainting Anti-Forensics Network via Attention-Guided Hierarchical Reconstruction. Symmetry, 15.","DOI":"10.3390\/sym15020393"},{"key":"ref_4","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., and Nie\u00dfner, M. (November, January 27). Faceforensics++: Learning to detect manipulated facial images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., and Lyu, S. (2020, January 13\u201319). Celeb-df: A large-scale challenging dataset for deepfake forensics. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","article-title":"Deepfakes and beyond: A survey of face manipulation and fake detection","volume":"64","author":"Tolosana","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1109\/TSP.2004.839932","article-title":"Exposing digital forgeries by detecting traces of resampling","volume":"53","author":"Popescu","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19007","DOI":"10.1007\/s10489-023-04462-2","article-title":"Tan-gfd: Generalizing face forgery detection based on texture information and adaptive noise mining","volume":"53","author":"Zhao","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_9","first-page":"362","article-title":"Detecting digital image forgeries using sensor pattern noise","volume":"Volume 6072","author":"Fridrich","year":"2006","journal-title":"Proceedings of the Security, Steganography, and Watermarking of Multimedia Contents VIII"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TIFS.2010.2051426","article-title":"JPEG error analysis and its applications to digital image forensics","volume":"5","author":"Luo","year":"2010","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I., and Davis, L.S. (2017, January 21\u201326). Two-stream neural networks for tampered face detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.229"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.eswa.2019.04.005","article-title":"Face image manipulation detection based on a convolutional neural network","volume":"129","author":"Dang","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4116","DOI":"10.1109\/TCSVT.2024.3522091","article-title":"Forgery-aware Adaptive Learning with Vision Transformer for Generalized Face Forgery Detection","volume":"35","author":"Luo","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s13735-025-00358-8","article-title":"Image forgery classification and localization through vision transformers","volume":"14","author":"Pawar","year":"2025","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chang, C.C., Lu, T.C., Zhu, Z.H., and Tian, H. (2018). An effective authentication scheme using DCT for mobile devices. Symmetry, 10.","DOI":"10.3390\/sym10010013"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Qian, Y., Yin, G., Sheng, L., Chen, Z., and Shao, J. (2020, January 23\u201328). Thinking in frequency: Face forgery detection by mining frequency-aware clues. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12167","DOI":"10.1109\/TKDE.2021.3117003","article-title":"Discriminative feature mining based on frequency information and metric learning for face forgery detection","volume":"35","author":"Li","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., and Ji, R. (2021, January 2\u20139). Local relation learning for face forgery detection. Proceedings of the AAAI Conference on Artificial Intelligence, Online.","DOI":"10.1609\/aaai.v35i2.16193"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., and Yu, N. (2021, January 20\u201325). Spatial-phase shallow learning: Rethinking face forgery detection in frequency domain. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/TIFS.2022.3233774","article-title":"F 2 trans: High-frequency fine-grained transformer for face forgery detection","volume":"18","author":"Miao","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_22","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_23","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"118423","DOI":"10.1016\/j.eswa.2022.118423","article-title":"ViXNet: Vision Transformer with Xception Network for deepfakes based video and image forgery detection","volume":"210","author":"Ganguly","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6418","DOI":"10.1109\/TCSVT.2023.3269841","article-title":"Interactive two-stream network across modalities for deepfake detection","volume":"33","author":"Wu","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1109\/TIFS.2022.3198275","article-title":"Hierarchical frequency-assisted interactive networks for face manipulation detection","volume":"17","author":"Miao","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_28","first-page":"5610611","article-title":"F3-Net: Multiview scene matching for drone-based geo-localization","volume":"61","author":"Sun","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., and Wei, Y. (2024, January 20\u201327). Frequency-aware deepfake detection: Improving generalizability through frequency space domain learning. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v38i5.28310"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7049","DOI":"10.1007\/s00371-024-03791-8","article-title":"Deepfake face detection via multi-level discrete wavelet transform and vision transformer","volume":"41","author":"Uddin","year":"2025","journal-title":"Vis. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Song, L., Fang, Z., Li, X., Dong, X., Jin, Z., Chen, Y., and Lyu, S. (2022, January 23\u201327). Adaptive face forgery detection in cross domain. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19830-4_27"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7943","DOI":"10.1109\/TCSVT.2023.3281475","article-title":"Spatial-temporal frequency forgery clue for video forgery detection in VIS and NIR scenario","volume":"33","author":"Wang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7578036","DOI":"10.1155\/2024\/7578036","article-title":"Deepfake Detection Based on the Adaptive Fusion of Spatial-Frequency Features","volume":"2024","author":"Wang","year":"2024","journal-title":"Int. J. Intell. Syst."},{"key":"ref_34","unstructured":"Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., and Wei, Y. (June, January 16\u2013). Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_35","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. proceedings, part III 18."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"119361","DOI":"10.1016\/j.eswa.2022.119361","article-title":"Rethinking gradient operator for exposing AI-enabled face forgeries","volume":"215","author":"Guo","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhang, Y., Yan, J., and Liu, W. (2021, January 20\u201325). Generalizing face forgery detection with high-frequency features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01605"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/7\/1148\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:11:47Z","timestamp":1760033507000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/7\/1148"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,18]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["sym17071148"],"URL":"https:\/\/doi.org\/10.3390\/sym17071148","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,7,18]]}}}