{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T01:42:53Z","timestamp":1784857373665,"version":"3.55.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031390586","type":"print"},{"value":"9783031390593","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-39059-3_3","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T13:01:37Z","timestamp":1690722097000},"page":"35-55","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards Exploring Adversarial Learning for\u00a0Anomaly Detection in\u00a0Complex Driving Scenes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6060-6177","authenticated-orcid":false,"given":"Nour","family":"Habib","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3052-040X","authenticated-orcid":false,"given":"Yunsu","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9503-9498","authenticated-orcid":false,"given":"Abhishek","family":"Buragohain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6850-6409","authenticated-orcid":false,"given":"Andreas","family":"Rausch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"3_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-20893-6_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Alexander, A., et al.: Variational autoencoder for end-to-end control of autonomous driving with novelty detection and training de-biasing, pp. 568\u2013575. IEEE (2018)","DOI":"10.1109\/IROS.2018.8594386"},{"issue":"1","key":"3_CR3","first-page":"1","volume":"2","author":"J An","year":"2015","unstructured":"An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2(1), 1\u201318 (2015)","journal-title":"Spec. Lect. IE"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Aniculaesei, A., Grieser, J., Rausch, A., Rehfeldt, K., Warnecke, T.: Towards a holistic software systems engineering approach for dependable autonomous systems. In: Stolle, R., Scholz, S., Broy, M. (eds.) Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, pp. 23\u201330. ACM, New York (2018). https:\/\/doi.org\/10.1145\/3194085.3194091","DOI":"10.1145\/3194085.3194091"},{"key":"3_CR5","unstructured":"Aniculaesei, A., Griesner, J., Rausch, A., Rehfeldt, K., Warnecke, T.: Graceful degradation of decision and control responsibility for autonomous systems based on dependability cages. In: 5th International Symposium on Future Active Safety Technology toward Zero, Blacksburg, Virginia, USA (2019)"},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Behere, S., T\u00f6rngren, M.: A functional architecture for autonomous driving. In: Kruchten, P., Dajsuren, Y., Altinger, H., Staron, M. (eds.) Proceedings of the First International Workshop on Automotive Software Architecture, pp. 3\u201310. ACM, New York (2015). https:\/\/doi.org\/10.1145\/2752489.2752491","DOI":"10.1145\/2752489.2752491"},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.infsof.2015.12.008","volume":"73","author":"S Behere","year":"2016","unstructured":"Behere, S., T\u00f6rngren, M.: A functional reference architecture for autonomous driving. Inf. Softw. Technol. 73, 136\u2013150 (2016). https:\/\/doi.org\/10.1016\/j.infsof.2015.12.008","journal-title":"Inf. Softw. Technol."},{"key":"3_CR8","unstructured":"Japkowicz, N., Myers, C., Gluck, M.: A novelty detection approach to classification. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 1, pp. 518\u2013523. Morgan Kaufmann Publishers Inc., San Francisco (1995)"},{"key":"3_CR9","unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: CIFAR: learning multiple layers of features from tiny images. https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html. Accessed 10 Feb 2023"},{"key":"3_CR10","unstructured":"LeCun, Y., Cortes, C., Burges, C., et al.: MNIST dataset of handwritten digits. https:\/\/www.tensorflow.org\/datasets\/catalog\/mnist. Accessed 10 Feb 2023"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Mahadevan, V., Li, W.X., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1975\u20131981 (2010)","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"3_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-45854-9","volume-title":"Autonomes Fahren","year":"2015","unstructured":"Maurer, M., Gerdes, J.C., Lenz, B., Winner, H. (eds.): Autonomes Fahren. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-45854-9"},{"key":"3_CR13","unstructured":"Mauritz, M., Rausch, A., Schaefer, I.: Dependable ADAS by combining design time testing and runtime monitoring. In: FORMS\/FORMAT 2014\u201310th Symposium on Formal Methods for Automation and Safety in Railway and Automotive Systems (2014)"},{"key":"3_CR14","unstructured":"Raulf, C., et al.: Dynamically configurable vehicle concepts for passenger transport. In: 13. Wissenschaftsforum Mobilit\u00e4t \u201cTransforming Mobility - What Next\u201d, Duisburg, Germany (2021)"},{"issue":"21","key":"3_CR15","doi-asserted-by":"publisher","first-page":"9881","DOI":"10.3390\/app11219881","volume":"11","author":"A Rausch","year":"2021","unstructured":"Rausch, A., Sedeh, A.M., Zhang, M.: Autoencoder-based semantic novelty detection: towards dependable AI-based systems. Appl. Sci. 11(21), 9881 (2021). https:\/\/doi.org\/10.3390\/app11219881","journal-title":"Appl. Sci."},{"key":"3_CR16","unstructured":"Rushby, J.: Quality measures and assurance for AI software, vol. 18 (1988)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3379\u20133388 (2018)","DOI":"10.1109\/CVPR.2018.00356"},{"key":"3_CR18","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. Adv. Neural Inf. Process. Syst. 29 (2016)"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. CoRR abs\/1703.05921 (2017). https:\/\/arxiv.org\/abs\/1703.05921","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"3_CR20","unstructured":"Sintini, L., Kunze, L.: Unsupervised and semi-supervised novelty detection using variational autoencoders in opportunistic science missions. In: BMVC (2020)"},{"issue":"1","key":"3_CR21","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1631\/FITEE.1700786","volume":"19","author":"H Wang","year":"2018","unstructured":"Wang, H., Li, X., Zhang, T.: Generative adversarial network based novelty detection usingminimized reconstruction error. Front. Inf. Technol. Electron. Eng. 19(1), 116\u2013125 (2018). https:\/\/doi.org\/10.1631\/FITEE.1700786","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"3_CR22","unstructured":"Youtie, J., Porter, A.L., Shapira, P., Woo, S., Huang, Y.: Autonomous systems: a bibliometric and patent analysis. Technical report, Exptertenkommission Forschung und Innovation (2017)"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., et al.: BDD100K: a diverse driving dataset for heterogeneous multitask learning (2020). https:\/\/bdd-data.berkeley.edu\/. Accessed 10 Feb 2023","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"3_CR24","unstructured":"Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. CoRR abs\/1802.06222 (2018). https:\/\/arxiv.org\/abs\/1802.06222"}],"container-title":["Communications in Computer and Information Science","Deep Learning Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39059-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T09:17:16Z","timestamp":1729847836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39059-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031390586","9783031390593"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39059-3_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeLTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Deep Learning Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"13 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"delta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/delta.scitevents.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","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":"9","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":"22","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":"21% - 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":"3","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}