{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:12:27Z","timestamp":1775196747407,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61802064"],"award-info":[{"award-number":["61802064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Fujian Agriculture and Forestry University Science and Technology Innovation Special Fund","award":["KFB23157A"],"award-info":[{"award-number":["KFB23157A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>As deepfakes become increasingly realistic, there is a growing need for robust and highly accurate facial forgery detection algorithms. Existing studies show that global feature modeling approaches (Transformer, VMamba) are effective in capturing long-range dependencies, yet they often lack sufficient sensitivity to localized facial tampering artifacts. Meanwhile, traditional convolutional methods excel at extracting local image features but struggle to incorporate prior knowledge about facial anatomy, resulting in limited representational capability. To address these limitations, this paper proposes LGMamba, a novel detection framework that integrates facial guidance focusing on key facial components and fine-grained detail regions commonly manipulated in deepfakes with global modeling. First, we introduce an innovative Landmark-Guided Convolution (LGConv), which adaptively adjusts convolutional sampling positions using facial landmark information. This allows the model to attend to forgery-prone facial regions, such as the eyes and mouth. Second, we design a parallel Facial Structure Awareness Block (FSAB) to operate alongside the VMamba-based visual State-Space Model. Equipped with a multi-stage residual design and a CBAM attention mechanism, FSAB enhances the model\u2019s sensitivity to subtle facial artifacts, enabling joint exploitation of global semantic consistency and fine-grained forgery cues within a unified architecture. The proposed LGMamba achieves superior performance compared to existing mainstream approaches. In cross-dataset evaluations, it attains AUC scores of 92.34% on CD1 and 96.01% on CD2, outperforming all compared methods.<\/jats:p>","DOI":"10.3390\/a19040270","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:09:21Z","timestamp":1775038161000},"page":"270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Generalizable Deepfake Detection via Facial Landmark-Guided Convolution and Local Structure Awareness"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5156-8374","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"first","affiliation":[{"name":"Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6783-1494","authenticated-orcid":false,"given":"Zhengxu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0178-5065","authenticated-orcid":false,"given":"Qin","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2529-0652","authenticated-orcid":false,"given":"Chunhui","family":"Feng","sequence":"additional","affiliation":[{"name":"Center for Agroforestry Mega Data Science, School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., and Guo, B. (2020, January 13\u201319). Face X-Ray for More General Face Forgery Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhu, H., Huang, H., Li, Y., Zheng, A., and He, R. (2020, January 11\u201317). Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, Vienna, Austria.","DOI":"10.24963\/ijcai.2020\/327"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, X., Zhu, J., Chu, W., Tai, Y., Li, J., Wang, C., Wu, Y., Huang, F., and Ji, R. (2021, January 19\u201327). HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/157"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"He, Y., Yu, N., Keuper, M., and Fritz, M. (2021, January 19\u201327). Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/349"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hu, Z., Xie, H., Wang, Y., Li, J., Wang, Z., and Zhang, Y. (2021, January 19\u201327). Dynamic Inconsistency-aware DeepFake Video Detection. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Montreal, QC, Canada.","DOI":"10.24963\/ijcai.2021\/102"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6111","DOI":"10.1109\/TPAMI.2021.3093446","article-title":"DeepFake Detection Based on Discrepancies Between Faces and Their Context","volume":"44","author":"Nirkin","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4131","DOI":"10.1109\/TCSVT.2020.3046240","article-title":"Spatiotemporal Trident Networks: Detection and Localization of Object Removal Tampering in Video Passive Forensics","volume":"31","author":"Yang","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, Y., Chang, M., and Lyu, S. (2018, January 11\u201313). In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking. Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security, WIFS 2018, Hong Kong, China.","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Matern, F., Riess, C., and Stamminger, M. (2019, January 7\u201311). Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations. Proceedings of the IEEE Winter Applications of Computer Vision Workshops, WACV Workshops 2019, Waikoloa Village, HI, USA.","DOI":"10.1109\/WACVW.2019.00020"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Akhtar, Z., and Dasgupta, D. (2019, January 5\u20136). A Comparative Evaluation of Local Feature Descriptors for DeepFakes Detection. Proceedings of the 2019 IEEE International Symposium on Technologies for Homeland Security (HST), Woburn, MA, USA.","DOI":"10.1109\/HST47167.2019.9033005"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., and Echizen, I. (2018, January 11\u201313). MesoNet: A Compact Facial Video Forgery Detection Network. Proceedings of the 2018 IEEE International Workshop on Information Forensics and Security, WIFS 2018, Hong Kong, China.","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hsu, C., Lee, C., and Zhuang, Y. (2018). Learning to Detect Fake Face Images in the Wild. arXiv.","DOI":"10.1109\/IS3C.2018.00104"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hsu, C.C., Zhuang, Y.X., and Lee, C.Y. (2020). Deep Fake Image Detection Based on Pairwise Learning. Appl. Sci., 10.","DOI":"10.3390\/app10010370"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Fang, F., Yamagishi, J., and Echizen, I. (2019, January 23\u201326). Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos. Proceedings of the 10th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2019, Tampa, FL, USA.","DOI":"10.1109\/BTAS46853.2019.9185974"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Yamagishi, J., and Echizen, I. (2019, January 12\u201317). Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2019, Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682602"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guera, D., and Delp, E.J. (2018, January 27\u201330). Deepfake Video Detection Using Recurrent Neural Networks. Proceedings of the 15th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2018, Auckland, New Zealand.","DOI":"10.1109\/AVSS.2018.8639163"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Amerini, I., Galteri, L., Caldelli, R., and Bimbo, A.D. (2019, January 27\u201328). Deepfake Video Detection through Optical Flow Based CNN. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshops, ICCV Workshops 2019, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00152"},{"key":"ref_18","unstructured":"Li, Y., and Lyu, S. (2019, January 16\u201320). Exposing DeepFake Videos By Detecting Face Warping Artifacts. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Long Beach, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","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 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00009"},{"key":"ref_20","first-page":"2638","article-title":"Domain General Face Forgery Detection by Learning to Weight","volume":"35","author":"Sun","year":"2021","journal-title":"Proc. Aaai Conf. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhou, P., Han, X., Morariu, V.I., and Davis, L.S. (2017). Two-Stream Neural Networks for Tampered Face Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2017, Honolulu, HI, USA, 21\u201326 July 2017, IEEE Computer Society.","DOI":"10.1109\/CVPRW.2017.229"},{"key":"ref_22","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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shiohara, K., and Yamasaki, T. (2022, January 18\u201324). Detecting Deepfakes with Self-Blended Images. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01816"},{"key":"ref_24","unstructured":"Ganiyusufoglu, I., Ng\u00f4, L.M., Savov, N., Karaoglu, S., and Gevers, T. (2020). Spatio-temporal Features for Generalized Detection of Deepfake Videos. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Amerini, I., and Caldelli, R. (2020, January 22\u201324). Exploiting Prediction Error Inconsistencies through LSTM-based Classifiers to Detect Deepfake Videos. Proceedings of the IH&MMSec \u201920: ACM Workshop on Information Hiding and Multimedia Security, Denver, CO, USA.","DOI":"10.1145\/3369412.3395070"},{"key":"ref_26","first-page":"667","article-title":"Two-Branch Recurrent Network for Isolating Deepfakes in Videos","volume":"Volume 12352","author":"Masi","year":"2020","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2020\u201416th European Conference, Glasgow, UK, 23\u201328 August 2020"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Haliassos, A., Vougioukas, K., Petridis, S., and Pantic, M. (2021, January 20\u201325). Lips Don\u2019t Lie: A Generalisable and Robust Approach to Face Forgery Detection. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"ref_28","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 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"ref_29","unstructured":"Cozzolino, D., Thies, J., R\u00f6ssler, A., Riess, C., Nie\u00dfner, M., and Verdoliva, L. (2018). ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1741","DOI":"10.1109\/TIFS.2022.3169921","article-title":"Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-Level Telltales","volume":"17","author":"Kong","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1109\/TIFS.2022.3146766","article-title":"ForgeryNIR: Deep Face Forgery and Detection in Near-Infrared Scenario","volume":"17","author":"Wang","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_32","unstructured":"Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., Jiao, J., and Liu, Y. (2024, January 10\u201315). VMamba: Visual State Space Model. Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017). Deformable Convolutional Networks. Proceedings of the IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22\u201329 October 2017, IEEE Computer Society.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bayar, B., and Stamm, M.C. (2016, January 20\u201322). A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer. Proceedings of the Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, IH&MMSec 2016, Vigo, Galicia, Spain.","DOI":"10.1145\/2909827.2930786"},{"key":"ref_35","first-page":"86","article-title":"Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues","volume":"Volume 12357","author":"Qian","year":"2020","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2020\u201416th European Conference, Glasgow, UK, 23\u201328 August 2020"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107616","DOI":"10.1016\/j.sigpro.2020.107616","article-title":"Identification of deep network generated images using disparities in color components","volume":"174","author":"Li","year":"2020","journal-title":"Signal Process."},{"key":"ref_37","unstructured":"Nirkin, Y., Wolf, L., Keller, Y., and Hassner, T. (2020). DeepFake Detection Based on the Discrepancy Between the Face and its Context. arXiv."},{"key":"ref_38","first-page":"101474","article-title":"Diffusionfake: Enhancing generalization in deepfake detection via guided stable diffusion","volume":"37","author":"Sun","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","unstructured":"Fu, X., Yan, Z., Yao, T., Chen, S., and Li, X. (March, January 25). Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing. Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yan, Z., Zhang, Y., Fan, Y., and Wu, B. (2023, January 1\u20136). UCF: Uncovering Common Features for Generalizable Deepfake Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV 2023, Paris, France.","DOI":"10.1109\/ICCV51070.2023.02048"},{"key":"ref_41","first-page":"111","article-title":"An Information Theoretic Approach for Attention-Driven Face Forgery Detection","volume":"Volume 13674","author":"Sun","year":"2022","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2022\u201417th European Conference, Tel Aviv, Israel, 23\u201327 October 2022"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Peng, S., Zhang, T., Gao, L., Zhu, X., Zhang, H., Pang, K., and Lei, Z. (2025, January 27\u201331). WMamba: Wavelet-based Mamba for Face Forgery Detection. Proceedings of the 33rd ACM International Conference on Multimedia, New York, NY, USA.","DOI":"10.1145\/3746027.3755592"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"114280","DOI":"10.1016\/j.knosys.2025.114280","article-title":"MSER-Net: Multi-stage edge refinement network for deepfake detection","volume":"328","author":"Zhang","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zakharov, E., Shysheya, A., Burkov, E., and Lempitsky, V.S. (November, January 27). Few-Shot Adversarial Learning of Realistic Neural Talking Head Models. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00955"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zeng, X., Wang, M., Pan, Y., Liu, L., Liu, Y., Ding, Y., and Fan, C. (2020, January 13\u201319). FReeNet: Multi-Identity Face Reenactment. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00537"},{"key":"ref_46","first-page":"690","article-title":"X2Face: A Network for Controlling Face Generation Using Images, Audio, and Pose Codes","volume":"Volume 11217","author":"Wiles","year":"2018","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, 8\u201314 September 2018"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hsu, G.S., Tsai, C.H., and Wu, H.Y. (2022, January 18\u201324). Dual-Generator Face Reenactment. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00072"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"9743","DOI":"10.1109\/TPAMI.2023.3253243","article-title":"Free-HeadGAN: Neural Talking Head Synthesis With Explicit Gaze Control","volume":"45","author":"Doukas","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, Z., Qi, X., and Torr, P.H. (2020, January 13\u201319). Global Texture Enhancement for Fake Face Detection in the Wild. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00808"},{"key":"ref_50","first-page":"363","article-title":"Wavelet Convolutions for Large Receptive Fields","volume":"Volume 15112","author":"Finder","year":"2024","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2024\u201418th European Conference, Milan, Italy, 29 September\u20134 October 2024"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., and Yang, G. (2023). Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, 1\u20136 October 2023, IEEE.","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"ref_52","first-page":"3","article-title":"CBAM: Convolutional Block Attention Module","volume":"Volume 11211","author":"Woo","year":"2018","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, 8\u201314 September 2018"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105190","DOI":"10.1016\/j.imavis.2024.105190","article-title":"LDConv: Linear deformable convolution for improving convolutional neural networks","volume":"149","author":"Zhang","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_54","unstructured":"Dolhansky, B., Howes, R., Pflaum, B., Baram, N., and Canton-Ferrer, C. (2019). The Deepfake Detection Challenge (DFDC) Preview Dataset. arXiv."},{"key":"ref_55","unstructured":"Dufour, G.R.N., and Gully, A. (2025, December 07). Contributing Data to Deepfake Detection Research. Available online: https:\/\/ai.googleblog.com\/2019\/09\/contributing-data-to-deepfake-detection.html."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/TCSVT.2022.3217950","article-title":"Artifacts-Disentangled Adversarial Learning for Deepfake Detection","volume":"33","author":"Li","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"106909","DOI":"10.1016\/j.neunet.2024.106909","article-title":"Towards generalizable face forgery detection via mitigating spurious correlation","volume":"182","author":"Bai","year":"2025","journal-title":"Neural Netw."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., and Yu, N. (2021, January 20\u201325). Multi-attentional Deepfake Detection. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cao, J., Ma, C., Yao, T., Chen, S., Ding, S., and Yang, X. (2022, January 18\u201324). End-to-End Reconstruction-Classification Learning for Face Forgery Detection. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00408"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1109\/TIFS.2023.3249566","article-title":"Masked Relation Learning for DeepFake Detection","volume":"18","author":"Yang","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s40747-024-01634-6","article-title":"Mf-net: Multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection","volume":"11","author":"Duan","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"103087","DOI":"10.1016\/j.inffus.2025.103087","article-title":"D2Fusion: Dual-domain fusion with feature superposition for Deepfake detection","volume":"120","author":"Qiu","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_63","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, USA. Proceedings of Machine Learning Research."},{"key":"ref_64","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 Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event.","DOI":"10.1609\/aaai.v35i2.16193"},{"key":"ref_65","unstructured":"Sun, K., Yao, T., Chen, S., Ding, S., Li, J., and Ji, R. (March, January 22). Dual Contrastive Learning for General Face Forgery Detection. Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022, Virtual Event."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Hu, J., Liao, X., Liang, J., Zhou, W., and Qin, Z. (March, January 22). FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos. Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022, Virtual Event.","DOI":"10.1609\/aaai.v36i1.19978"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Dong, S., Wang, J., Ji, R., Liang, J., Fan, H., and Ge, Z. (2023, January 17\u201324). Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00389"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Nguyen, D., Mejri, N., Singh, I.P., Kuleshova, P., Astrid, M., Kacem, A., Ghorbel, E., and Aouada, D. (2024, January 16\u201322). LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01647"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Yan, Z., Luo, Y., Lyu, S., Liu, Q., and Wu, B. (2024, January 16\u201322). Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00858"},{"key":"ref_70","unstructured":"Yang, L., Zhang, R.Y., Li, L., and Xie, X. (2021, January 18\u201324). SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Proceedings of the 38th International Conference on Machine Learning, Virtual. Proceedings of Machine Learning Research."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, IEEE Computer Society.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_73","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 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/270\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:15:20Z","timestamp":1775189720000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,1]]},"references-count":74,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["a19040270"],"URL":"https:\/\/doi.org\/10.3390\/a19040270","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,1]]}}}