{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:01:02Z","timestamp":1743062462353,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819997879"},{"type":"electronic","value":"9789819997886"}],"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\/978-981-99-9788-6_19","type":"book-chapter","created":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T17:02:14Z","timestamp":1706979734000},"page":"219-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Anomaly Detection and\u00a0Localization Method Based on\u00a0Feature Fusion and\u00a0Attention"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5463-1348","authenticated-orcid":false,"given":"Zixi","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4483-8599","authenticated-orcid":false,"given":"Xin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0002-3478","authenticated-orcid":false,"given":"Dengquan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6254-3747","authenticated-orcid":false,"given":"Shenping","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0636-5695","authenticated-orcid":false,"given":"Tijian","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,4]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Sig. Process. 99(6), 215\u2013249 (2014)","DOI":"10.1016\/j.sigpro.2013.12.026"},{"key":"19_CR2","unstructured":"Yang, F., Peng, Y., Li, Y.: Research on insulator self-explosion detection with small sample based on deep learning. J. East China Jiaotong Univ. 2, 110\u2013117 (2022)"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Walluscheck, S., Canalini, L., Klein, J., Heldmann, S.: Unsupervised learning of healthy anatomy for anomaly detection in brain CT scans. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 12465, p. 1246504 (2023)","DOI":"10.1117\/12.2653889"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Tong, H.Z.: Research on multiple classification detection for network traffic anomaly based on deep learning. In: 2022 6th International Symposium on Computer Science and Intelligent Control (ISCSIC), pp. 12\u201316, November 2022","DOI":"10.1109\/ISCSIC57216.2022.00014"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Tao, X., Gong, X., Zhang, X.Y., Yan, S., Adak, C.: Deep learning for unsupervised anomaly localization in industrial images: a survey. IEEE Trans. Instrum. Measur. 71, 1\u201321 (2022)","DOI":"10.1109\/TIM.2022.3196436"},{"key":"19_CR6","unstructured":"Ruff, L., Vandermeulen, R.A., G\u00f6rnitz, N., Deecke, L., Kloft, M.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393\u20134402, July 2018"},{"key":"19_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2020.11.018","volume":"424","author":"Y Shi","year":"2021","unstructured":"Shi, Y., Yang, J., Qi, Z.: Unsupervised anomaly segmentation via deep feature reconstruction. Neurocomputing 424, 9\u201322 (2021)","journal-title":"Neurocomputing"},{"key":"19_CR8","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"19_CR9","unstructured":"Dinh, L., Sohl-Dickstein, J.N., Bengio, S.: Density estimation using Real NVP (2016). https:\/\/arxiv.org\/abs\/1605.08803"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Gudovskiy, D., Ishizaka, S., Kozuka, K.: CFLOW-AD: real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 98\u2013107 (2022)","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3606\u20133613 (2014)","DOI":"10.1109\/CVPR.2014.461"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Hou, Q.B., Zhou, D.Q., Feng, J.S.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13708\u201313717 (2021)","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2011\u20132023 (2017)","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD\u2014a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9584\u20139592 (2019)","DOI":"10.1109\/CVPR.2019.00982"},{"key":"19_CR15","unstructured":"Pang, G., Ding, C., Shen, C., Hengel, A.V.: Explainable deep few-shot anomaly detection with deviation networks (2021). https:\/\/arxiv.org\/abs\/2108.00462"},{"key":"19_CR16","unstructured":"You, Z.Y., Cui, L., Shen, Y., Yang, K., Lu, X., Zheng, Y.: A unified model for multi-class anomaly detection. In: Advances in Neural Information Processing Systems, vol. 35, pp. 4571\u20134584 (2022)"},{"key":"19_CR17","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Wang, X., Deng, R., Bao, T., Zhao, R., Wu, L.: Focus your distribution: coarse-to-fine non-contrastive learning for anomaly detection and localization. In: 2022 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2022)","DOI":"10.1109\/ICME52920.2022.9859925"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: DRAEM \u2013 a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8310\u20138319 (2021)","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14897\u201314907 (2021)","DOI":"10.1109\/CVPR46437.2021.01466"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence Security and Privacy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9788-6_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T17:04:35Z","timestamp":1706979875000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9788-6_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819997879","9789819997886"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9788-6_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":"4 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIS&P","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence Security and Privacy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ais&p2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/aisp2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"115","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23 large model and security workshop papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}