{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:04:08Z","timestamp":1784203448482,"version":"3.55.0"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010909","name":"Excellent Young Scientists Fund","doi-asserted-by":"publisher","award":["2025JJ40066"],"award-info":[{"award-number":["2025JJ40066"]}],"id":[{"id":"10.13039\/501100010909","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72571281"],"award-info":[{"award-number":["72571281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.neunet.2026.109027","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T23:02:22Z","timestamp":1776985342000},"page":"109027","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Towards few-shot deepfake detection with an enhanced CLIP model"],"prefix":"10.1016","volume":"202","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5761-4853","authenticated-orcid":false,"given":"Yumin","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2394-3518","authenticated-orcid":false,"given":"Xueyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2161-3591","authenticated-orcid":false,"given":"Bo","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9184-5313","authenticated-orcid":false,"given":"Yanming","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109027_bib0001","doi-asserted-by":"crossref","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018). Mesonet: A compact facial video forgery detection network. 2018 IEEE international workshop on information forensics and security (WIFS), (1\u20137). https:\/\/api.semanticscholar.org\/CorpusID:52157475.","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"10.1016\/j.neunet.2026.109027_bib0002","unstructured":"Aneja, S., & Niebner, M. (2020). Generalized zero and few-shot transfer for facial forgery detection. https:\/\/arxiv.org\/abs\/2006.11863."},{"key":"10.1016\/j.neunet.2026.109027_bib0003","doi-asserted-by":"crossref","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 Networks"},{"key":"10.1016\/j.neunet.2026.109027_bib0004","series-title":"2022\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"18689","article-title":"Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection","author":"Chen","year":"2022"},{"key":"10.1016\/j.neunet.2026.109027_bib0005","unstructured":"Cheng, X., Wang, H., Luo, X., Guan, Q., Ma, B., & Wang, J. (2025). Re-cropping framework: A grid recovery method for quantization step estimation in non-aligned recompressed images. IEEE Transactions on Circuits and Systems for Video Technology, (1\u20131). 10.1109\/TCSVT.2025.3635150."},{"key":"10.1016\/j.neunet.2026.109027_bib0006","series-title":"2023\u202fIEEE\/CVF international conference on computer vision (ICCV)","first-page":"20585","article-title":"Transface: Calibrating transformer training for face recognition from a data-centric perspective","author":"Dan","year":"2023"},{"key":"10.1016\/j.neunet.2026.109027_bib0007","unstructured":"Deepfakes. faceswap. https:\/\/github.com\/deepfakes\/faceswap. Accessed: 2021-04-24."},{"key":"10.1016\/j.neunet.2026.109027_bib0008","unstructured":"Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., & Canton-Ferrer, C. (2020). The deepfake detection challenge dataset. arXiv:abs\/2006.07397, https:\/\/api.semanticscholar.org\/CorpusID:219687616."},{"issue":"2","key":"10.1016\/j.neunet.2026.109027_bib0009","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s11263-023-01891-x","article-title":"Clip-adapter: Better vision-language models with feature adapters","volume":"132","author":"Gao","year":"2023","journal-title":"International Journal of Computer Vision"},{"issue":"6","key":"10.1016\/j.neunet.2026.109027_bib0010","doi-asserted-by":"crossref","first-page":"8011","DOI":"10.1109\/TDSC.2025.3603570","article-title":"Encrypt a story: A video segment encryption method based on the discrete sinusoidal memristive rulkov neuron","volume":"22","author":"Gao","year":"2025","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"issue":"11","key":"10.1016\/j.neunet.2026.109027_bib0011","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Communications of the ACM"},{"issue":"21","key":"10.1016\/j.neunet.2026.109027_bib0012","doi-asserted-by":"crossref","DOI":"10.3390\/s23218763","article-title":"Mcw: A generalizable deepfake detection method for few-shot learning","volume":"23","author":"Guan","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.neunet.2026.109027_bib0013","series-title":"2025\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"105","article-title":"Rethinking vision-language model in face forensics: Multi-modal interpretable forged face detector","author":"Guo","year":"2025"},{"key":"10.1016\/j.neunet.2026.109027_bib0014","series-title":"Computer vision - ECCV 2022: 17th European conference, Tel Aviv, Israel, October 23\u201327, 2022, proceedings, part XXXIII","first-page":"709","article-title":"Visual prompt tuning","author":"Jia","year":"2022"},{"key":"10.1016\/j.neunet.2026.109027_bib0015","series-title":"2025\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"8775","article-title":"Freqdebias: Towards generalizable deepfake detection via consistency-driven frequency debiasing","author":"Kashiani","year":"2025"},{"key":"10.1016\/j.neunet.2026.109027_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111451","article-title":"Diffface: Diffusion-based face swapping with facial guidance","volume":"163","author":"Kim","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109027_bib0017","unstructured":"Kingma, D. P., & Welling, M. (2022). Auto-encoding variational bayes. https:\/\/arxiv.org\/abs\/1312.6114."},{"issue":"1","key":"10.1016\/j.neunet.2026.109027_bib0018","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-23920-0","article-title":"Deepfake video deception detection using visual attention-based method","volume":"15","author":"Lal","year":"2025","journal-title":"Scientific Reports"},{"key":"10.1016\/j.neunet.2026.109027_bib0019","series-title":"2020\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"5000","article-title":"Face x-ray for more general face forgery detection","author":"Li","year":"2020"},{"key":"10.1016\/j.neunet.2026.109027_bib0020","series-title":"Companion proceedings of the web conference 2020","first-page":"88","article-title":"Fighting against deepfake: Patch&pair convolutional neural networks (PPCNN)","author":"Li","year":"2020"},{"key":"10.1016\/j.neunet.2026.109027_bib0021","series-title":"CVPR workshops","article-title":"Exposing deepfake videos by detecting face warping artifacts","author":"Li","year":"2018"},{"key":"10.1016\/j.neunet.2026.109027_bib0022","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2019). Celeb-DF: A new dataset for deepfake forensics. arXiv:abs\/1909.12962, https:\/\/api.semanticscholar.org\/CorpusID:203593264."},{"key":"10.1016\/j.neunet.2026.109027_bib0023","series-title":"AAAI conference on artificial intelligence","article-title":"Standing on the shoulders of giants: Reprogramming visual-language model for general deepfake detection","author":"Lin","year":"2024"},{"key":"10.1016\/j.neunet.2026.109027_bib0024","series-title":"2021\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"772","article-title":"Spatial-phase shallow learning: Rethinking face forgery detection in frequency domain","author":"Liu","year":"2021"},{"issue":"8","key":"10.1016\/j.neunet.2026.109027_bib0025","doi-asserted-by":"crossref","first-page":"5449","DOI":"10.1109\/TPAMI.2024.3366769","article-title":"From simple to complex scenes: Learning robust feature representations for accurate human parsing","volume":"46","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109027_bib0026","series-title":"ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks","author":"Lu","year":"2019"},{"key":"10.1016\/j.neunet.2026.109027_bib0027","series-title":"2021\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"16312","article-title":"Generalizing face forgery detection with high-frequency features","author":"Luo","year":"2021"},{"key":"10.1016\/j.neunet.2026.109027_bib0028","unstructured":"MarekKowalski. Faceswap. https:\/\/www.github.com\/MarekKowalski\/FaceSwap. Accessed 2021-04-24."},{"key":"10.1016\/j.neunet.2026.109027_bib0029","series-title":"Pattern recognition","first-page":"386","article-title":"Ldfacenet: Latent diffusion-based network for high-fidelity deepfake generation","author":"Mehta","year":"2025"},{"key":"10.1016\/j.neunet.2026.109027_bib0030","series-title":"ICASSP 2019 - 2019 IEEE international conference on acoustics, speech and signal processing (ICASSP)","first-page":"2307","article-title":"Capsule-forensics: Using capsule networks to detect forged images and videos","author":"Nguyen","year":"2019"},{"issue":"3","key":"10.1016\/j.neunet.2026.109027_bib0031","doi-asserted-by":"crossref","first-page":"5295","DOI":"10.32604\/cmc.2024.049911","article-title":"A deepfake detection algorithm based on fourier transform of biological signal","volume":"79","author":"Ni","year":"2024","journal-title":"Computers, Materials and Continua"},{"key":"10.1016\/j.neunet.2026.109027_bib0032","series-title":"2023\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"24480","article-title":"Towards universal fake image detectors that generalize across generative models","author":"Ojha","year":"2023"},{"key":"10.1016\/j.neunet.2026.109027_bib0033","series-title":"IEEE conference on computer vision and pattern recognition","article-title":"Cats and dogs","author":"Parkhi","year":"2012"},{"issue":"1","key":"10.1016\/j.neunet.2026.109027_bib0034","doi-asserted-by":"crossref","first-page":"467","DOI":"10.32604\/cmc.2023.042417","article-title":"Multi-branch deepfake detection algorithm based on fine-grained features","volume":"77","author":"Qin","year":"2023","journal-title":"Computers, Materials & Continua"},{"key":"10.1016\/j.neunet.2026.109027_bib0035","series-title":"Proceedings of the 38th international conference on machine learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume":"vol. 139","author":"Radford","year":"2021"},{"key":"10.1016\/j.neunet.2026.109027_bib0036","series-title":"2019\u202fIEEE\/CVF international conference on computer vision (ICCV)","first-page":"1","article-title":"Faceforensics++: Learning to detect manipulated facial images","author":"R\u00f6ssler","year":"2019"},{"key":"10.1016\/j.neunet.2026.109027_bib0037","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":"2016","journal-title":"International Journal of Computer Vision"},{"key":"10.1016\/j.neunet.2026.109027_bib0038","unstructured":"Shi, L., & Fu, Y. (2025). Expertgen: Training-free expert guidance for controllable text-to-face generation. arXiv:abs\/2505.17256, https:\/\/api.semanticscholar.org\/CorpusID:278886713."},{"key":"10.1016\/j.neunet.2026.109027_bib0039","unstructured":"Silva, S., Ali, E., Arora, C., & Khan, M. H. (2025). microCLIP: Unsupervised CLIP adaptation via coarse-fine token fusion for fine-grained image classification. arXiv:abs\/2510.02270, https:\/\/api.semanticscholar.org\/CorpusID:281725144."},{"key":"10.1016\/j.neunet.2026.109027_bib0040","series-title":"Proceedings of the 31st international conference on neural information processing systems","first-page":"4080","article-title":"Prototypical networks for few-shot learning","author":"Snell","year":"2017"},{"key":"10.1016\/j.neunet.2026.109027_bib0041","series-title":"2025\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"19576","article-title":"Towards general visual-linguistic face forgery detection","author":"Sun","year":"2025"},{"key":"10.1016\/j.neunet.2026.109027_bib0042","series-title":"AAAI conference on artificial intelligence","doi-asserted-by":"crossref","DOI":"10.5772\/intechopen.94615","article-title":"Domain general face forgery detection by learning to weight","author":"Sun","year":"2021"},{"issue":"4","key":"10.1016\/j.neunet.2026.109027_bib0043","doi-asserted-by":"crossref","DOI":"10.1145\/3306346.3323035","article-title":"Deferred neural rendering: Image synthesis using neural textures","volume":"38","author":"Thies","year":"2019","journal-title":"ACM Transactions on Graphics"},{"key":"10.1016\/j.neunet.2026.109027_bib0044","series-title":"2016\u202fIEEE conference on computer vision and pattern recognition (CVPR)","first-page":"2387","article-title":"Face2face: Real-time face capture and reenactment of RGB videos","author":"Thies","year":"2016"},{"issue":"6","key":"10.1016\/j.neunet.2026.109027_bib0045","doi-asserted-by":"crossref","first-page":"5861","DOI":"10.1109\/TDSC.2025.3576223","article-title":"Light-field image multiple reversible robust watermarking against geometric attacks","volume":"22","author":"Wang","year":"2025","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"10.1016\/j.neunet.2026.109027_bib0046","unstructured":"Wu, S., Liu, J., Li, J., & Wang, Y. (2025). Few-shot learner generalizes across AI-generated image detection. https:\/\/arxiv.org\/abs\/2501.08763."},{"key":"10.1016\/j.neunet.2026.109027_bib0047","series-title":"2024\u202fIEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"8984","article-title":"Transcending forgery specificity with latent space augmentation for generalizable deepfake detection","author":"Yan","year":"2024"},{"key":"10.1016\/j.neunet.2026.109027_bib0048","unstructured":"Yan, Z., Wang, J., Jin, P., Zhang, K.-Y., Liu, C., Chen, S., Yao, T., Ding, S., Wu, B., & Yuan, L. (2025). Orthogonal subspace decomposition for generalizable AI-generated image detection. https:\/\/arxiv.org\/abs\/2411.15633."},{"key":"10.1016\/j.neunet.2026.109027_bib0049","series-title":"2023\u202fIEEE\/CVF international conference on computer vision (ICCV)","first-page":"22355","article-title":"Ucf: Uncovering common features for generalizable deepfake detection","author":"Yan","year":"2023"},{"key":"10.1016\/j.neunet.2026.109027_bib0050","unstructured":"Yan, Z., Zhang, Y., Yuan, X., Lyu, S., & Wu, B. (2023b). Deepfakebench: A comprehensive benchmark of deepfake detection. arXiv:abs\/2307.01426, https:\/\/api.semanticscholar.org\/CorpusID:259342157."},{"key":"10.1016\/j.neunet.2026.109027_bib0051","series-title":"2024\u202fIEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW)","first-page":"1593","article-title":"Low-rank few-shot adaptation of vision-language models","author":"Zanella","year":"2024"},{"key":"10.1016\/j.neunet.2026.109027_bib0052","series-title":"Computer vision \u2013 ECCV 2022","first-page":"493","article-title":"Tip-adapter: Training-free adaption of CLIP for few-shot classification","author":"Zhang","year":"2022"},{"issue":"5","key":"10.1016\/j.neunet.2026.109027_bib0053","doi-asserted-by":"crossref","first-page":"6259","DOI":"10.1007\/s11042-021-11733-y","article-title":"Deepfake generation and detection, a survey","volume":"81","author":"Zhang","year":"2022","journal-title":"Multimedia Tools and Applications"},{"issue":"9","key":"10.1016\/j.neunet.2026.109027_bib0054","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","article-title":"Learning to prompt for vision-language models","volume":"130","author":"Zhou","year":"2022","journal-title":"International Journal of Computer Vision"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026004879?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026004879?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:13:43Z","timestamp":1784200423000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026004879"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":54,"alternative-id":["S0893608026004879"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109027","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Towards few-shot deepfake detection with an enhanced CLIP model","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109027","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109027"}}