{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:19:46Z","timestamp":1783455586220,"version":"3.55.0"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62502344"],"award-info":[{"award-number":["62502344"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402255"],"award-info":[{"award-number":["62402255"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U25A20444"],"award-info":[{"award-number":["U25A20444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372325"],"award-info":[{"award-number":["62372325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024QF020"],"award-info":[{"award-number":["ZR2024QF020"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin Municipality","doi-asserted-by":"publisher","award":["23JCZDJC00280"],"award-info":[{"award-number":["23JCZDJC00280"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011160","name":"State Key Laboratory of Virtual Reality Technology and Systems","doi-asserted-by":"publisher","award":["VRLAB2025C05"],"award-info":[{"award-number":["VRLAB2025C05"]}],"id":[{"id":"10.13039\/501100011160","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114315","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:27:16Z","timestamp":1782491236000},"page":"114315","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["Prior-knowledge guidance and dual-domain representation refinement for deepfake detection"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6546-4216","authenticated-orcid":false,"given":"Hao","family":"Jia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunjie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.patcog.2026.114315_b1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"10.1016\/j.patcog.2026.114315_b2","unstructured":"R. Robin, B. Andreas, L. Dominik, E. Patrick, O. Bj\u00f6rn, High-resolution image synthesis with latent diffusion models, in: Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684\u201310695."},{"key":"10.1016\/j.patcog.2026.114315_b3","doi-asserted-by":"crossref","unstructured":"B. Weiming, L. Yufan, Z. Zhipeng, L. Bing, H. Weiming, AUNet: Learning Relations Between Action Units for Face Forgery Detection, in: Conference on Computer Vision and Pattern Recognition, 2023, pp. 24709\u201324719.","DOI":"10.1109\/CVPR52729.2023.02367"},{"key":"10.1016\/j.patcog.2026.114315_b4","doi-asserted-by":"crossref","unstructured":"D. Shichao, W. Jin, J. Renhe, L. Jiajun, F. Haoqiang, G. Zheng, Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization, in: Conference on Computer Vision and Pattern Recognition, 2023, pp. 3994\u20134004.","DOI":"10.1109\/CVPR52729.2023.00389"},{"issue":"4","key":"10.1016\/j.patcog.2026.114315_b5","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1007\/s12559-024-10287-z","article-title":"A review of key technologies for emotion analysis using multimodal information","volume":"16","author":"Zhu","year":"2024","journal-title":"Cogn. Comput."},{"key":"10.1016\/j.patcog.2026.114315_b6","first-page":"1","article-title":"TEMPO: Training-time equilibration of modalities for per-sample optimization in multimodal sentiment","author":"Zhao","year":"2026","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.patcog.2026.114315_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103268","article-title":"RMER-DT: Robust multimodal emotion recognition in conversational contexts based on diffusion and transformers","volume":"123","author":"Zhu","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114315_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103809","article-title":"Integrating audio-visual text generation with contrastive learning for enhanced multimodal emotion analysis","volume":"127","author":"Xiang","year":"2026","journal-title":"Inf. Fusion"},{"issue":"8","key":"10.1016\/j.patcog.2026.114315_b9","doi-asserted-by":"crossref","DOI":"10.1007\/s40747-025-01931-8","article-title":"EMVAS: end-to-end multimodal emotion visualization analysis system","volume":"11","author":"Zhu","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.patcog.2026.114315_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108564","article-title":"A client-server based recognition system: Non-contact single\/multiple emotional and behavioral state assessment methods","volume":"260","author":"Zhu","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.patcog.2026.114315_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112755","article-title":"ADNet: Delving into generalizable deepfake detection via adaptive expert selection and discrepancy learning","volume":"173","author":"Chen","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114315_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111528","article-title":"Leveraging facial landmarks improves generalization ability for deepfake detection","volume":"164","author":"Gao","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114315_b13","doi-asserted-by":"crossref","unstructured":"T. Chuangchuang, Z. Yao, W. Shikui, G. Guanghua, L. Ping, W. Yunchao, Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection, in: Conference on Computer Vision and Pattern Recognition, 2024, pp. 28130\u201328139.","DOI":"10.1109\/CVPR52733.2024.02657"},{"key":"10.1016\/j.patcog.2026.114315_b14","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.1109\/TIFS.2024.3361151","article-title":"Mmnet: multi-collaboration and multi-supervision network for sequential deepfake detection","volume":"19","author":"Ruiyang","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.patcog.2026.114315_b15","doi-asserted-by":"crossref","unstructured":"L. Lingzhi, B. Jianmin, Z. Ting, Y. Hao, C. Dong, W. Fang, G. Baining, Face x-ray for more general face forgery detection, in: Conference on Computer Vision and Pattern Recognition, 2020, pp. 5001\u20135010.","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"10.1016\/j.patcog.2026.114315_b16","unstructured":"S. Kaede, Y. Toshihiko, Detecting deepfakes with self-blended images, in: Conference on Computer Vision and Pattern Recognition, 2022, pp. 18720\u201318729."},{"key":"10.1016\/j.patcog.2026.114315_b17","series-title":"27th IEEE\/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing, SNPD 2024 Summer, Beijing, China, July 5-7, 2024","first-page":"50","article-title":"A face forgery video detection model based on knowledge distillation","author":"Liang","year":"2024"},{"issue":"14","key":"10.1016\/j.patcog.2026.114315_b18","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s11227-025-07795-6","article-title":"Interactive dual-branch network based on adversarial knowledge distillation for compressed deepfake detection","volume":"81","author":"Yang","year":"2025","journal-title":"J. Supercomput."},{"key":"10.1016\/j.patcog.2026.114315_b19","series-title":"IEEE Conference on Computer Vision and Pattern Recognition Workshops","first-page":"1001","article-title":"FReTAL: Generalizing deepfake detection using knowledge distillation and representation learning","author":"Kim","year":"2021"},{"key":"10.1016\/j.patcog.2026.114315_b20","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/LSP.2025.3540941","article-title":"Cross-domain deepfake detection based on latent domain knowledge distillation","volume":"32","author":"Wang","year":"2025","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.patcog.2026.114315_b21","series-title":"Proceedings of the 33rd ACM International Conference on Multimedia","first-page":"11318","article-title":"Knowledge negative distillation: Circumventing overfitting to unlock more generalizable deepfake detection","author":"Liu","year":"2025"},{"key":"10.1016\/j.patcog.2026.114315_b22","doi-asserted-by":"crossref","unstructured":"R. Andreas, C. Davide, V. Luisa, R. Christian, T. Justus, N. Matthias, FaceForensics++: Learning to Detect Manipulated Facial Images, in: International Conference on Computer Vision, 2019, pp. 1\u201311.","DOI":"10.1109\/ICCV.2019.00009"},{"key":"10.1016\/j.patcog.2026.114315_b23","series-title":"DeepFakes","year":"2021"},{"key":"10.1016\/j.patcog.2026.114315_b24","doi-asserted-by":"crossref","unstructured":"T. Justus, Z. Michael, S. Marc, T. Christian, N. Matthias, Face2face: Real-time face capture and reenactment of rgb videos, in: Conference on Computer Vision and Pattern Recognition, 2016, pp. 2387\u20132395.","DOI":"10.1109\/CVPR.2016.262"},{"key":"10.1016\/j.patcog.2026.114315_b25","series-title":"FaceSwap","year":"2020"},{"issue":"4","key":"10.1016\/j.patcog.2026.114315_b26","first-page":"1","article-title":"Deferred neural rendering: Image synthesis using neural textures","volume":"38","author":"Justus","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.patcog.2026.114315_b27","doi-asserted-by":"crossref","unstructured":"Y. Li, X. Yang, P. Sun, H. Qi, S. Lyu, Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics, in: Conference on Computer Vision and Pattern Recognition, 2020, pp. 3204\u20133213.","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"10.1016\/j.patcog.2026.114315_b28","series-title":"Contributing data to deepfake detection research","author":"Research","year":"2021"},{"key":"10.1016\/j.patcog.2026.114315_b29","series-title":"The DeepFake detection challenge (DFDC) dataset","first-page":"1","author":"Dolhansky","year":"2019"},{"key":"10.1016\/j.patcog.2026.114315_b30","doi-asserted-by":"crossref","unstructured":"L. Yuezun, C. Ming-Ching, L. Siwei, In ictu oculi: Exposing ai created fake videos by detecting eye blinking, in: IEEE International Workshop on Information Forensics and Security, 2018, pp. 1\u20137.","DOI":"10.1109\/WIFS.2018.8630787"},{"key":"10.1016\/j.patcog.2026.114315_b31","doi-asserted-by":"crossref","unstructured":"L. Lingzhi, B. Jianmin, Y. Hao, C. Dong, W. Fang, Advancing high fidelity identity swapping for forgery detection, in: Conference on Computer Vision and Pattern Recognition, 2020, pp. 5074\u20135083.","DOI":"10.1109\/CVPR42600.2020.00512"},{"key":"10.1016\/j.patcog.2026.114315_b32","doi-asserted-by":"crossref","unstructured":"Y. Zhiyuan, Y. Taiping, C. Shen, Z. Yandan, F. Xinghe, Z. Junwei, L. Donghao, W. Chengjie, D. Shouhong, W. Yunsheng, Y. Li, DF40: Toward Next-Generation Deepfake Detection, in: Advances in Neural Information Processing Systems, Vol. 37, 2024, pp. 29387\u201329434.","DOI":"10.52202\/079017-0925"},{"key":"10.1016\/j.patcog.2026.114315_b33","doi-asserted-by":"crossref","unstructured":"Y. Zhiyuan, Z. Yong, Y. Xinhang, L. Siwei, W. Baoyuan, DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection, in: Advances in Neural Information Processing Systems, Vol. 36, 2023, pp. 4534\u20134565.","DOI":"10.52202\/075280-0201"},{"key":"10.1016\/j.patcog.2026.114315_b34","unstructured":"T. Mingxing, L. Quoc, Efficientnet: Rethinking model scaling for convolutional neural networks, in: International Conference on Machine Learning, 2019, pp. 6105\u20136114."},{"key":"10.1016\/j.patcog.2026.114315_b35","doi-asserted-by":"crossref","unstructured":"Q. Yuyang, Y. Guojun, S. Lu, C. Zixuan, S. Jing, Thinking in frequency: Face forgery detection by mining frequency-aware clues, in: European Conference on Computer Vision, 2020, pp. 86\u2013103.","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"10.1016\/j.patcog.2026.114315_b36","doi-asserted-by":"crossref","unstructured":"L. Honggu, L. Xiaodan, Z. Wenbo, C. Yuefeng, H. Yuan, X. Hui, Z. Weiming, Y. Nenghai, Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain, in: Conference on Computer Vision and Pattern Recognition, 2021, pp. 772\u2013781.","DOI":"10.1109\/CVPR46437.2021.00083"},{"key":"10.1016\/j.patcog.2026.114315_b37","unstructured":"L. Yuchen, Z. Yong, Y. Junchi, L. Wei, Generalizing face forgery detection with high-frequency features, in: Conference on Computer Vision and Pattern Recognition, 2021, pp. 16317\u201316326."},{"key":"10.1016\/j.patcog.2026.114315_b38","doi-asserted-by":"crossref","unstructured":"N. Yunsheng, M. Depu, Y. Changqian, Q. Chengbin, R. Dongchun, Z. Youjian, Core: Consistent representation learning for face forgery detection, in: Conference on Computer Vision and Pattern Recognition, 2022, pp. 12\u201321.","DOI":"10.1109\/CVPRW56347.2022.00011"},{"key":"10.1016\/j.patcog.2026.114315_b39","unstructured":"C. Junyi, M. Chao, Y. Taiping, C. Shen, D. Shouhong, Y. Xiaokang, End-to-end reconstruction-classification learning for face forgery detection, in: Conference on Computer Vision and Pattern Recognition, 2022, pp. 4113\u20134122."},{"key":"10.1016\/j.patcog.2026.114315_b40","unstructured":"Y. Zhiyuan, Z. Yong, F. Yanbo, W. Baoyuan, UCF: Uncovering Common Features for Generalizable Deepfake Detection, in: International Conference on Computer Vision, 2023, pp. 22412\u201322423."},{"key":"10.1016\/j.patcog.2026.114315_b41","doi-asserted-by":"crossref","unstructured":"Z. Yan, Y. Luo, S. Lyu, Q. Liu, B. Wu, Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection, in: Conference on Computer Vision and Pattern Recognition, 2024, pp. 8984\u20138994.","DOI":"10.1109\/CVPR52733.2024.00858"},{"key":"10.1016\/j.patcog.2026.114315_b42","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TIFS.2023.3324739","article-title":"Constructing new backbone networks via space-frequency interactive convolution for deepfake detection","volume":"19","author":"Guo","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.patcog.2026.114315_b43","series-title":"IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"1576","article-title":"Wavelet-driven generalizable framework for deepfake face forgery detection","author":"Baru","year":"2025"},{"issue":"6","key":"10.1016\/j.patcog.2026.114315_b44","doi-asserted-by":"crossref","first-page":"5489","DOI":"10.1109\/TCSVT.2025.3530402","article-title":"LGDF-Net: Local and global feature-based dual-branch fusion networks for deepfake detection","volume":"35","author":"Long","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"3","key":"10.1016\/j.patcog.2026.114315_b45","doi-asserted-by":"crossref","first-page":"3011","DOI":"10.1109\/TDSC.2024.3523289","article-title":"ADA-FInfer: Inferring face representations from adaptive select frames for high-visual-quality deepfake detection","volume":"22","author":"Hu","year":"2025","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"10.1016\/j.patcog.2026.114315_b46","series-title":"Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features","author":"Tschannen","year":"2025"},{"key":"10.1016\/j.patcog.2026.114315_b47","series-title":"Meta clip 2: A worldwide scaling recipe","author":"Chuang","year":"2025"},{"key":"10.1016\/j.patcog.2026.114315_b48","series-title":"Decoupled weight decay regularization","first-page":"1","author":"Ilya","year":"2017"},{"key":"10.1016\/j.patcog.2026.114315_b49","unstructured":"J. Liming, L. Ren, W. Wayne, Q. Chen, L.C. Change, Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection, in: Conference on Computer Vision and Pattern Recognition, 2020, pp. 2889\u20132898."},{"key":"10.1016\/j.patcog.2026.114315_b50","doi-asserted-by":"crossref","unstructured":"S.R. R., C. Michael, D. Abhishek, V. Ramakrishna, P. Devi, B. Dhruv, Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization, in: International Conference on Computer Vision, 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S003132032601280X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S003132032601280X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T19:55:28Z","timestamp":1783454128000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S003132032601280X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":50,"alternative-id":["S003132032601280X"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114315","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Prior-knowledge guidance and dual-domain representation refinement for deepfake detection","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114315","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"114315"}}