{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:40:56Z","timestamp":1778085656060,"version":"3.51.4"},"reference-count":93,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["U24B20179"],"award-info":[{"award-number":["U24B20179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing NSF","award":["L242021"],"award-info":[{"award-number":["L242021"]}]},{"name":"Talent Fund of BJTU","award":["2024XKRC047"],"award-info":[{"award-number":["2024XKRC047"]}]},{"name":"Open Project of Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University","award":["MMC202406"],"award-info":[{"award-number":["MMC202406"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2025.3581726","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T17:27:06Z","timestamp":1750440426000},"page":"6429-6442","source":"Crossref","is-referenced-by-count":5,"title":["SAGNet: Decoupling Semantic-Agnostic Artifacts From Limited Training Data for Robust Generalization in Deepfake Detection"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5695-2009","authenticated-orcid":false,"given":"Renshuai","family":"Tao","sequence":"first","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9342-1407","authenticated-orcid":false,"given":"Chuangchuang","family":"Tan","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8830-170X","authenticated-orcid":false,"given":"Huan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5884-3412","authenticated-orcid":false,"given":"Jiakai","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhongguancun Laboratory, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7391-7539","authenticated-orcid":false,"given":"Haotong","family":"Qin","sequence":"additional","affiliation":[{"name":"Center for Project-Based Learning (PBL), ETH Zürich, Zürich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2384-386X","authenticated-orcid":false,"given":"Yakun","family":"Chang","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5477-1017","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5096-8752","authenticated-orcid":false,"given":"Rongrong","family":"Ni","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8581-9554","authenticated-orcid":false,"given":"Yao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02345"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3268843"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3268532"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3320612"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3372798"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3239223"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3301451"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3386058"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3331240"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2010.2101310"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3318939"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3262112"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3389572"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01816"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01165"},{"key":"ref17","article-title":"ForensicTransfer: Weakly-supervised domain adaptation for forgery detection","author":"Cozzolino","year":"2018","journal-title":"arXiv:1812.02510"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2455334"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICME51207.2021.9428429"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095167"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3329426"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3658664.3659635"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3625231"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02094"},{"key":"ref25","article-title":"Fake or JPEG? Revealing common biases in generated image detection datasets","author":"Grommelt","year":"2024","journal-title":"arXiv:2403.17608"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3443650"},{"key":"ref27","article-title":"Combating mode collapse in GAN training: An empirical analysis using Hessian eigenvalues","author":"Durall","year":"2020","journal-title":"arXiv:2012.09673"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00791"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2901826"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3129654"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00102"},{"key":"ref32","article-title":"Unmasking DeepFakes with simple features","author":"Durall","year":"2019","journal-title":"arXiv:1911.00686"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1111\/1365-2478.12889"},{"key":"ref34","article-title":"Detecting AutoAttack perturbations in the frequency domain","author":"Lorenz","year":"2021","journal-title":"arXiv:2111.08785"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01468"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01815"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00408"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/349"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19781-9_5"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00293"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19990"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9897820"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00402"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02051"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00771"},{"key":"ref46","article-title":"Towards the detection of diffusion model deepfakes","author":"Ricker","year":"2022","journal-title":"arXiv:2210.14571"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS47025.2019.9035107"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS47025.2019.9035099"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00104"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19784-0_39"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01383"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICME52920.2022.9859763"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02657"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref55","first-page":"3247","article-title":"Leveraging frequency analysis for deep fake image recognition","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Frank"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_7"},{"key":"ref58","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref59","first-page":"1","article-title":"Progressive growing of GANs for improved quality, stability, and variation","volume-title":"Proc. ICLR","author":"Karras"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref62","first-page":"1","article-title":"Large scale GAN training for high fidelity natural image synthesis","volume-title":"Proc. ICLR","author":"Brock"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00244"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00009"},{"key":"ref67","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","author":"Yu","year":"2015","journal-title":"arXiv:1506.03365"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref71","volume-title":"Gangen-Detection: A Dataset Generated by Gans for Generalizable Deepfake Detection","author":"Tan","year":"2024"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2916751"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1703.10717"},{"key":"ref74","article-title":"The Cramer distance as a solution to biased Wasserstein gradients","author":"Bellemare","year":"2017","journal-title":"arXiv:1705.10743"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00399"},{"key":"ref76","first-page":"2203","article-title":"MMD GAN: Towards deeper understanding of moment matching network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Li"},{"key":"ref77","first-page":"1","article-title":"RelGAN: Relational generative adversarial networks for text generation","volume-title":"Proc. ICLR","author":"Nie"},{"key":"ref78","first-page":"4183","article-title":"High-fidelity image generation with fewer labels","volume-title":"Proc. ICML","author":"Lu\u010di\u0107"},{"key":"ref79","article-title":"Spectral normalization for generative adversarial networks","author":"Miyato","year":"2018","journal-title":"arXiv:1802.05957"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00379"},{"key":"ref81","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. NIPS","volume":"34","author":"Dhariwal"},{"key":"ref82","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. NIPS","volume":"33","author":"Ho"},{"key":"ref83","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Nichol"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref85","first-page":"1","article-title":"Pseudo numerical methods for diffusion models on manifolds","volume-title":"Proc. ICLR","author":"Liu"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01043"},{"key":"ref87","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. ICLR","author":"Kingma"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01024"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i1.32063"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19886"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02045"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/11045799.pdf?arnumber=11045799","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T06:03:09Z","timestamp":1751090589000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11045799\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":93,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3581726","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}