{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:34:20Z","timestamp":1785512060216,"version":"3.56.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031409523","type":"print"},{"value":"9783031409530","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\/978-3-031-40953-0_33","type":"book-chapter","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T17:02:08Z","timestamp":1694624528000},"page":"390-396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluating and\u00a0Increasing Segmentation Robustness in\u00a0CARLA"],"prefix":"10.1007","author":[{"given":"Venkatesh","family":"Thirugnana Sambandham","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantin","family":"Kirchheim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Ortmeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,14]]},"reference":[{"issue":"17","key":"33_CR1","doi-asserted-by":"publisher","first-page":"2132","DOI":"10.3390\/electronics10172132","volume":"10","author":"KD Apostolidis","year":"2021","unstructured":"Apostolidis, K.D., Papakostas, G.A.: A survey on adversarial deep learning robustness in medical image analysis. Electronics 10(17), 2132 (2021)","journal-title":"Electronics"},{"key":"33_CR2","unstructured":"Bai, Y., Mei, J., Yuille, A.L., Xie, C.: Are transformers more robust than CNNs? In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 26831\u201326843. Curran Associates, Inc. (2021)"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Bhojanapalli, S., Chakrabarti, A., Glasner, D., Li, D., Unterthiner, T., Veit, A.: Understanding robustness of transformers for image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10231\u201310241 (2021)","DOI":"10.1109\/ICCV48922.2021.01007"},{"key":"33_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1007\/978-3-030-01090-4_8","volume-title":"Automated Technology for Verification and Analysis","author":"C-H Cheng","year":"2018","unstructured":"Cheng, C.-H., Huang, C.-H., Yasuoka, H.: Quantitative projection coverage for testing ML-enabled autonomous systems. In: Lahiri, S.K., Wang, C. (eds.) ATVA 2018. LNCS, vol. 11138, pp. 126\u2013142. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01090-4_8"},{"key":"33_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth $$16\\times 16$$ words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"issue":"1","key":"33_CR6","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","volume":"16","author":"T Falk","year":"2019","unstructured":"Falk, T., et al.: U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16(1), 67\u201370 (2019)","journal-title":"Nat. Methods"},{"issue":"8","key":"33_CR7","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1167\/9.8.1037","volume":"9","author":"L Fei-Fei","year":"2009","unstructured":"Fei-Fei, L., Deng, J., Li, K.: ImageNet: constructing a large-scale image database. J. Vis. 9(8), 1037\u20131037 (2009)","journal-title":"J. Vis."},{"key":"33_CR8","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015)"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"33_CR10","unstructured":"Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: Proceedings of the International Conference on Learning Representations (2019)"},{"key":"33_CR11","unstructured":"Hsuan-Cheng Liao, B., Cheng, C.H., Esen, H., Knoll, A.: Are transformers more robust? Towards exact robustness verification for transformers. arXiv e-prints, pp. arXiv-2202 (2022)"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Kamann, C., Rother, C.: Benchmarking the robustness of semantic segmentation models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8828\u20138838 (2020)","DOI":"10.1109\/CVPR42600.2020.00885"},{"key":"33_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Michaelis, C., et al.: Benchmarking robustness in object detection: autonomous driving when winter is coming. In: Machine Learning for Autonomous Driving Workshop, NeurIPS 2019, vol. 190707484 (2019)","DOI":"10.12792\/icisip2019.002"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Nesti, F., Rossolini, G., Nair, S., Biondi, A., Buttazzo, G.: Evaluating the robustness of semantic segmentation for autonomous driving against real-world adversarial patch attacks. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2280\u20132289 (2022)","DOI":"10.1109\/WACV51458.2022.00288"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Schwalbe, G., Schels, M.: A survey on methods for the safety assurance of machine learning based systems. In: 10th European Congress on Embedded Real Time Software and Systems (ERTS 2020) (2020)","DOI":"10.20378\/irb-47275"},{"key":"33_CR19","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"33_CR20","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"33_CR22","doi-asserted-by":"publisher","unstructured":"Zhou, X., Kouzel, M., Alemzadeh, H.: Robustness testing of data and knowledge driven anomaly detection in cyber-physical systems. In: 2022 52nd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), pp. 44\u201351 (2022). https:\/\/doi.org\/10.1109\/DSN-W54100.2022.00017","DOI":"10.1109\/DSN-W54100.2022.00017"}],"container-title":["Lecture Notes in Computer Science","Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40953-0_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T17:06:56Z","timestamp":1694624816000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40953-0_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031409523","9783031409530"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40953-0_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAFECOMP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Safety, Reliability, and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toulouse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"19 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"safecomp2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/safecomp2023.cnrs.fr\/","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":"100","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":"20","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":"20% - 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":"5.7","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":"For the workshops, 35 out of 49 papers have been accepted","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)"}}]}}