{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:55:42Z","timestamp":1781884542297,"version":"3.54.5"},"reference-count":35,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T00:00:00Z","timestamp":1698364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["U21B2020"],"award-info":[{"award-number":["U21B2020"]}]},{"name":"the National Natural Science Foundation of China","award":["U1936216"],"award-info":[{"award-number":["U1936216"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of deepfake technology, deepfake detection has received widespread attention. Although some deepfake forensics techniques have been proposed, they are still very difficult to implement in real-world scenarios. This is due to the differences in different deepfake technologies and the compression or editing of videos during the propagation process. Considering the issue of sample imbalance with few-shot scenarios in deepfake detection, we propose a multi-feature channel domain-weighted framework based on meta-learning (MCW). In order to obtain outstanding detection performance of a cross-database, the proposed framework improves a meta-learning network in two ways: it enhances the model\u2019s feature extraction ability for detecting targets by combining the RGB domain and frequency domain information of the image and enhances the model\u2019s generalization ability for detecting targets by assigning meta weights to channels on the feature map. The proposed MCW framework solves the problems of poor detection performance and insufficient data compression resistance of the algorithm for samples generated by unknown algorithms. The experiment was set in a zero-shot scenario and few-shot scenario, simulating the deepfake detection environment in real situations. We selected nine detection algorithms as comparative algorithms. The experimental results show that the MCW framework outperforms other algorithms in cross-algorithm detection and cross-dataset detection. The MCW framework demonstrates its ability to generalize and resist compression with low-quality training images and across different generation algorithm scenarios, and it has better fine-tuning potential in few-shot learning scenarios.<\/jats:p>","DOI":"10.3390\/s23218763","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T11:50:18Z","timestamp":1698407418000},"page":"8763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["MCW: A Generalizable Deepfake Detection Method for Few-Shot Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Lei","family":"Guan","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9126-3054","authenticated-orcid":false,"given":"Yifan","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.inffus.2020.06.014","article-title":"Deepfake and beyond: A survey of face manipulation and fake detection","volume":"64","author":"Tolosana","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17521","DOI":"10.1007\/s11042-022-13797-w","article-title":"Image forgery detection: A survey of recent deep-learning approaches","volume":"82","author":"Zanardelli","year":"2022","journal-title":"Multimedia Tools Appl."},{"key":"ref_3","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_4","unstructured":"Le, T.N., Nguyen, H.H., Yamagishi, J., and Echizen, I. (2022). Frontiers in Fake Media Generation and Detection, Springer Nature."},{"key":"ref_5","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), Hong Kong, China.","DOI":"10.1109\/WIFS.2018.8630761"},{"key":"ref_6","unstructured":"Afchar, D., Nozick, V., Yamagishi, J., and Echizen, I. (2019). Use of a capsule network to detect fake images and videos. arXiv."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"14859","DOI":"10.1007\/s11042-022-13966-x","article-title":"SRTNet: A spatial and residual based two-stream neural network for deepfakes detection","volume":"82","author":"Zhang","year":"2022","journal-title":"Multimedia Tools Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Matern, F., Riess, C., and Stamminger, M. (2019, January 7\u201311). Exploiting visual artifacts to expose deepfake and face manipulations. Proceedings of the 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW), Waikoloa Village, HI, USA.","DOI":"10.1109\/WACVW.2019.00020"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yang, X., Li, Y., and Lyu, S. (2019, January 12\u201317). Exposing deep fakes using inconsistent head poses. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683164"},{"key":"ref_11","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00808"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, X., Karaman, S., and Chang, S.F. (2019, January 9\u201312). Detecting and simulating artifacts in gan fake images. Proceedings of the 2019 IEEE International Workshop on Information Forensics and Security (WIFS), Delft, The Netherlands.","DOI":"10.1109\/WIFS47025.2019.9035107"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, J., Xie, H., Li, J., Wang, Z., and Zhang, Y. (2021, January 20\u201325). Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00639"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Wang, C., and Deng, W. (2021, January 20\u201325). Representative forgery mining for fake face detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01468"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zi, B., Chang, M., Chen, J., Ma, X., and Jiang, Y.G. (2020, January 12\u201316). Wilddeepfake: A challenging real-world dataset for DeepFake detection. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413769"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhu, X., Wang, H., Fei, H., Lei, Z., and Li, S.Z. (2021, January 20\u201325). Face forgery detection by 3d decomposition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00295"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dang, H., Liu, F., Stehouwer, J., Liu, X., and Jain, A.K. (2020, January 13\u201319). On the detection of digital face manipulation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00582"},{"key":"ref_19","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 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), Tampa, FL, USA.","DOI":"10.1109\/BTAS46853.2019.9185974"},{"key":"ref_20","first-page":"1","article-title":"Generalizing from a few examples: A survey on few-shot learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv. (Csur)"},{"key":"ref_21","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qiu, H., Chen, S., Gan, B., Wang, K., Shi, H., Shao, J., and Liu, Z. (2022). Few-shot Forgery Detection via Guided Adversarial Interpolation. arXiv.","DOI":"10.2139\/ssrn.4373079"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, Y.-K., and Sun, H.-L. (2023). Few-Shot Training GAN for Face Forgery Classification and Segmentation Based on the Fine-Tune Approach. Electronics, 12.","DOI":"10.3390\/electronics12061417"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, Y.-K., and Yen, T.-Y. (2023). A Meta-Learning Approach for Few-Shot Face Forgery Segmentation and Classification. Sensors, 23.","DOI":"10.3390\/s23073647"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sun, K., Liu, H., Ye, Q., Gao, Y., Liu, J., Shao, L., and Ji, R. (2021, January 2\u20139). Domain general face forgery detection by learning to weight. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i3.16367"},{"key":"ref_26","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_27","unstructured":"Aneja, S., and Nie\u00dfner, M. (2020). Generalized zero and few-shot transfer for facial forgery detection. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, K., Li, Z., and Qiao, Y. (2016, January 11\u201314). A discriminative feature learning approach for deep face recognition. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part VII 14.","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"ref_29","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_30","unstructured":"Dolhansky, B., Howes, R., Pflaum, B., Baram, N., and Ferrer, C.C. (2019). The DeepFake detection challenge (dfdc) preview dataset. arXiv."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Xu, Y., Raja, K., and Pedersen, M. (2022, January 3\u20138). Supervised contrastive learning for generalizable and explainable deepfake detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACVW54805.2022.00044"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., and Hospedales, T. (2018, January 2\u20137). Learning to generalize: Meta-learning for domain generalization. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"ref_34","first-page":"4077","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., and Hospedales, T.M. (2018, January 18\u201323). Learning to compare: Relation network for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/21\/8763\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:12:49Z","timestamp":1760130769000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/21\/8763"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,27]]},"references-count":35,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23218763"],"URL":"https:\/\/doi.org\/10.3390\/s23218763","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,27]]}}}