{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:51:17Z","timestamp":1743101477480,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031723346"},{"type":"electronic","value":"9783031723353"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72335-3_19","type":"book-chapter","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T14:03:01Z","timestamp":1726495381000},"page":"275-288","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalizable Deepfake Detection with\u00a0Unbiased Feature Extraction and\u00a0Low-Level Forgery Enhancement"],"prefix":"10.1007","author":[{"given":"Zhihan","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JiaXin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangshuo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuesheng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guibo","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Chen, H.S., Rouhsedaghat, M., Ghani, H., Hu, S., You, S., Kuo, C.C.J.: DefakeHop: a light-weight high-performance DeepFake detector. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp.\u00a01\u20136. IEEE (2021)","DOI":"10.1109\/ICME51207.2021.9428361"},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., Ji, R.: Local relation learning for face forgery detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 1081\u20131088 (2021)","DOI":"10.1609\/aaai.v35i2.16193"},{"key":"19_CR3","unstructured":"Dolhansky, B., et al.: The DeepFake detection challenge (DFDC) dataset. arXiv preprint arXiv:2006.07397 (2020)"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Dong, S., Wang, J., Ji, R., Liang, J., Fan, H., Ge, Z.: Implicit identity leakage: the stumbling block to improving DeepFake detection generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3994\u20134004 (2023)","DOI":"10.1109\/CVPR52729.2023.00389"},{"key":"19_CR5","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16\u00a0$$\\times $$\u00a016 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Fei, J., Dai, Y., Yu, P., Shen, T., Xia, Z., Weng, J.: Learning second order local anomaly for general face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20270\u201320280 (2022)","DOI":"10.1109\/CVPR52688.2022.01963"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Haliassos, A., Vougioukas, K., Petridis, S., Pantic, M.: Lips don\u2019t lie: a generalisable and robust approach to face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5039\u20135049 (2021)","DOI":"10.1109\/CVPR46437.2021.00500"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Jeong, Y., Kim, D., Min, S., Joe, S., Gwon, Y., Choi, J.: BiHPF: bilateral high-pass filters for robust DeepFake detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 48\u201357 (2022)","DOI":"10.1109\/WACV51458.2022.00293"},{"key":"19_CR10","unstructured":"Koopman, M., Rodriguez, A.M., Geradts, Z.: Detection of DeepFake video manipulation. In: The 20th Irish Machine Vision and Image Processing Conference (IMVIP), pp. 133\u2013136 (2018)"},{"key":"19_CR11","doi-asserted-by":"publisher","first-page":"107616","DOI":"10.1016\/j.sigpro.2020.107616","volume":"174","author":"H Li","year":"2020","unstructured":"Li, H., Li, B., Tan, S., Huang, J.: Identification of deep network generated images using disparities in color components. Signal Process. 174, 107616 (2020)","journal-title":"Signal Process."},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Face X-ray for more general face forgery detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5001\u20135010 (2020)","DOI":"10.1109\/CVPR42600.2020.00505"},{"key":"19_CR13","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-DF (v2): a new dataset for DeepFake forensics. arXiv preprint arXiv (2019)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-DF: a large-scale challenging dataset for DeepFake forensics. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3207\u20133216 (2020)","DOI":"10.1109\/CVPR42600.2020.00327"},{"key":"19_CR15","first-page":"23296","volume":"34","author":"MM Naseer","year":"2021","unstructured":"Naseer, M.M., Ranasinghe, K., Khan, S.H., Hayat, M., Shahbaz Khan, F., Yang, M.H.: Intriguing properties of vision transformers. Adv. Neural. Inf. Process. Syst. 34, 23296\u201323308 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Ojha, U., Li, Y., Lee, Y.J.: Towards universal fake image detectors that generalize across generative models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24480\u201324489 (2023)","DOI":"10.1109\/CVPR52729.2023.02345"},{"key":"19_CR17","unstructured":"Oquab, M., et\u00a0al.: DINOv2: learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"19_CR18","doi-asserted-by":"publisher","unstructured":"P\u00e9rez, J.C., et al.: Gabor layers enhance network robustness. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Proceedings, Part IX 16, pp. 450\u2013466. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_26","DOI":"10.1007\/978-3-030-58545-7_26"},{"key":"19_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-58610-2_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Qian","year":"2020","unstructured":"Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: face forgery detection by mining frequency-aware clues. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 86\u2013103. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_6"},{"key":"19_CR20","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nie\u00dfner, M.: FaceForensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1\u201311 (2019)","DOI":"10.1109\/ICCV.2019.00009"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Shiohara, K., Yamasaki, T.: Detecting DeepFakes with self-blended images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18720\u201318729 (2022)","DOI":"10.1109\/CVPR52688.2022.01816"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Shuai, C., et al.: Locate and verify: a two-stream network for improved DeepFake detection. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7131\u20137142 (2023)","DOI":"10.1145\/3581783.3612386"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Sun, K., Yao, T., Chen, S., Ding, S., Li, J., Ji, R.: Dual contrastive learning for general face forgery detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 2316\u20132324 (2022)","DOI":"10.1609\/aaai.v36i2.20130"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Wang, C., Deng, W.: Representative forgery mining for fake face detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14923\u201314932 (2021)","DOI":"10.1109\/CVPR46437.2021.01468"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: M2TR: multi-modal multi-scale transformers for DeepFake detection. In: Proceedings of the 2022 International Conference on Multimedia Retrieval, pp. 615\u2013623 (2022)","DOI":"10.1145\/3512527.3531415"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Wang, W., et\u00a0al.: InternImage: exploring large-scale vision foundation models with deformable convolutions. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14408\u201314419 (2023)","DOI":"10.1109\/CVPR52729.2023.01385"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional DeepFake detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2185\u20132194 (2021)","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for DeepFake detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15023\u201315033 (2021)","DOI":"10.1109\/ICCV48922.2021.01475"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Zou, B., Yang, C., Guan, J., Quan, C., Zhao, Y.: DFCP: few-shot DeepFake detection via contrastive pretraining. In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pp. 2303\u20132308. IEEE (2023)","DOI":"10.1109\/ICME55011.2023.00393"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72335-3_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T14:17:56Z","timestamp":1726496276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72335-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723346","9783031723353"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72335-3_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lugano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}