{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:09:20Z","timestamp":1743030560996,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":78,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819612413"},{"type":"electronic","value":"9789819612420"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-1242-0_28","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T08:06:30Z","timestamp":1733990790000},"page":"372-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transferable Attacks for\u00a0Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Mengqi","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"28_CR1","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (2018)"},{"key":"28_CR2","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: IEEE International Conference on Computer Vision (ICCV), pp. 12\u00a0179\u201312\u00a0188 (2021)","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, X., Wang, J.: Object-contextual representations for semantic segmentation. In: European Conference on Computer Vision (ECCV), pp. 173\u2013190 (2020)","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"28_CR5","unstructured":"Yan, H., Zhang, C., Wu, M.: Lawin transformer: improving semantic segmentation transformer with multi-scale representations via large window attention. CoRR, vol. abs\/2201.01615 (2022). arxiv:2201.01615"},{"issue":"5","key":"28_CR6","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1007\/s11263-021-01445-z","volume":"129","author":"R Mohan","year":"2021","unstructured":"Mohan, R., Valada, A.: Efficientps: efficient panoptic segmentation. Int. J. Comput. Vis. 129(5), 1551\u20131579 (2021)","journal-title":"Int. J. Comput. Vis."},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, B., et al.: Panoptic-deeplab: a simple, strong, and fast baseline for bottom-up panoptic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12\u00a0475\u201312\u00a0485 (2020)","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Dcnas: densely connected neural architecture search for semantic image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13\u00a0956\u201313\u00a0967 (2021)","DOI":"10.1109\/CVPR46437.2021.01374"},{"key":"28_CR9","doi-asserted-by":"publisher","first-page":"6829","DOI":"10.1109\/TIP.2021.3099366","volume":"30","author":"X Li","year":"2021","unstructured":"Li, X., et al.: Global aggregation then local distribution for scene parsing. IEEE Trans. Image Process. 30, 6829\u20136842 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"28_CR10","unstructured":"Chen, L., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. CoRR, vol. abs\/1706.05587 (2017). arxiv:1706.05587"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: multi-path refinement networks for high-resolution semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1925\u20131934 (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"28_CR14","unstructured":"Li, P., Dong, X., Yu, X., Yang, Y.: When humans meet machines: towards efficient segmentation networks. In: British Machine Vision Virtual Conference (2020)"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Ding, Y., Yu, X., Yang, Y.: Modeling the probabilistic distribution of unlabeled data for one-shot medical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 2, pp. 1246\u20131254 (2021)","DOI":"10.1609\/aaai.v35i2.16212"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Ding, Y., Yu, X., Yang, Y.: Rfnet: region-aware fusion network for incomplete multi-modal brain tumor segmentation. In: ICCV, pp. 3975\u20133984 (2021)","DOI":"10.1109\/ICCV48922.2021.00394"},{"key":"28_CR17","unstructured":"Khan, M.W., et\u00a0al.: RVD: a handheld device-based fundus video dataset for retinal vessel segmentation. In: Advances in Neural Information Processing Systems, vol.\u00a036 (2024)"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Acuna, D., Kar, A., Fidler, S.: Devil is in the edges: learning semantic boundaries from noisy annotations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11\u00a0075\u201311\u00a0083 (2019)","DOI":"10.1109\/CVPR.2019.01133"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"issue":"4","key":"28_CR20","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L Chen","year":"2018","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 40(4), 834\u2013848 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"issue":"10","key":"28_CR21","doi-asserted-by":"publisher","first-page":"4350","DOI":"10.1109\/TITS.2019.2939832","volume":"21","author":"L Deng","year":"2019","unstructured":"Deng, L., Yang, M., Li, H., Li, T., Hu, B., Wang, C.: Restricted deformable convolution-based road scene semantic segmentation using surround view cameras. IEEE Trans. Intell. Transp. Syst. 21(10), 4350\u20134362 (2019)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"28_CR22","unstructured":"Liu, C., et al.: Benchmarking audio visual segmentation for long-untrimmed videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22\u00a0712\u201322\u00a0722 (2024)"},{"issue":"11","key":"28_CR23","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"28_CR24","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (ICLR) (2014)"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, J., Kreis, K., Torralba, A., Fidler, S.: Semantic segmentation with generative models: semi-supervised learning and strong out-of-domain generalization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8296\u20138307 (2021)","DOI":"10.1109\/CVPR46437.2021.00820"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Souly, N., Spampinato, C., Shah, M.: Semi supervised semantic segmentation using generative adversarial network. In: IEEE International Conference on Computer Vision (ICCV), pp. 5689\u20135697 (2017)","DOI":"10.1109\/ICCV.2017.606"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Liu, C., et al.: Audio-visual segmentation by exploring cross-modal mutual semantics. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7590\u20137598 (2023)","DOI":"10.1145\/3581783.3612373"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Du, H., Yu, X., Hussain, F., Armin, M.A., Petersson, L., Li, W.: Weakly-supervised point cloud instance segmentation with geometric priors. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 4271\u20134280 (2023)","DOI":"10.1109\/WACV56688.2023.00425"},{"key":"28_CR29","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s12021-018-9377-x","volume":"16","author":"Y Xue","year":"2018","unstructured":"Xue, Y., Xu, T., Zhang, H., Long, L.R., Huang, X.: Segan: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 16, 383\u2013392 (2018)","journal-title":"Neuroinformatics"},{"key":"28_CR30","unstructured":"Zhaoa, Z., Wang, Y., Liu, K. Yang, H., Sun, Q., Qiao, H.: Semantic segmentation by improved generative adversarial networks. CoRR, vol. abs\/2104.09917 (2021). arxiv:2104.09917"},{"key":"28_CR31","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: IEEE International Conference on Computer Vision (ICCV), pp. 1378\u20131387 (2017)","DOI":"10.1109\/ICCV.2017.153"},{"key":"28_CR32","doi-asserted-by":"crossref","unstructured":"Arnab, A., Miksik, O., Torr, P.H.: On the robustness of semantic segmentation models to adversarial attacks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 888\u2013897 (2018)","DOI":"10.1109\/CVPR.2018.00099"},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Gu, J., Zhao, H., Tresp, V., Torr, P.H.: Segpgd: an effective and efficient adversarial attack for evaluating and boosting segmentation robustness. In: European Conference on Computer Vision (ECCV), pp. 308\u2013325 (2022)","DOI":"10.1007\/978-3-031-19818-2_18"},{"key":"28_CR34","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations (ICLR) (2014)"},{"key":"28_CR35","unstructured":"Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. CoRR, vol. abs\/1608.04644 (2016). arxiv:1608.04644"},{"key":"28_CR36","unstructured":"Tram\u00e8r, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., McDaniel, P.: Ensemble adversarial training: attacks and defenses (2017). arxiv:1705.07204"},{"key":"28_CR37","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2574\u20132582 (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"28_CR38","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V., Fidler, S.: Gated-SCNN: gated shape CNNs for semantic segmentation. In: IEEE International Conference on Computer Vision (ICCV), pp. 5228\u20135237 (2019)","DOI":"10.1109\/ICCV.2019.00533"},{"key":"28_CR39","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Xie, J., Chen, X., Wang, J.: Segfix: model-agnostic boundary refinement for segmentation. In: European Conference on Computer Vision (ECCV), pp. 489\u2013506 (2020)","DOI":"10.1007\/978-3-030-58610-2_29"},{"key":"28_CR40","doi-asserted-by":"crossref","unstructured":"Liang, J., Homayounfar, N., Ma, W.-C., Xiong, Y., Hu, R., Urtasun, R.: Polytransform: deep polygon transformer for instance segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9128\u20139137 (2020)","DOI":"10.1109\/CVPR42600.2020.00915"},{"key":"28_CR41","doi-asserted-by":"crossref","unstructured":"Wang, P., et al.: Understanding convolution for semantic segmentation. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1451\u20131460. IEEE (2018)","DOI":"10.1109\/WACV.2018.00163"},{"key":"28_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"28_CR43","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: IEEE International Conference on Computer Vision (ICCV), pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"28_CR44","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Luo, P., Loy, C.-C., Tang, X.: Semantic image segmentation via deep parsing network. In: IEEE International Conference on Computer Vision (ICCV), pp. 1377\u20131385 (2015)","DOI":"10.1109\/ICCV.2015.162"},{"key":"28_CR45","doi-asserted-by":"crossref","unstructured":"Lin, G., Shen, C., Van Den\u00a0Hengel, A., Reid, I.: Efficient piecewise training of deep structured models for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3194\u20133203 (2016)","DOI":"10.1109\/CVPR.2016.348"},{"key":"28_CR46","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 109\u2013117 (2011)"},{"key":"28_CR47","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 5998\u20136008 (2017)"},{"key":"28_CR48","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. CoRR, vol. abs\/2102.04306 (2021). arxiv:2102.04306"},{"key":"28_CR49","doi-asserted-by":"crossref","unstructured":"Strudel, R., Pinel, R.G., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: IEEE International Conference on Computer Vision (ICCV), pp. 7242\u20137252 (2021)","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"28_CR50","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"28_CR51","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., \u00c1lvarez, J.M., Luo, P.: Segformer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 12\u00a0077\u201312\u00a0090 (2021)"},{"key":"28_CR52","unstructured":"Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 17\u00a0864\u201317\u00a0875 (2021)"},{"key":"28_CR53","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1290\u20131299 (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"28_CR54","doi-asserted-by":"crossref","unstructured":"Hendrik\u00a0Metzen, J., Chaithanya\u00a0Kumar, M., Brox, T., Fischer, V.: Universal adversarial perturbations against semantic image segmentation. In: IEEE International Conference on Computer Vision (ICCV), pp. 2755\u20132764 (2017)","DOI":"10.1109\/ICCV.2017.300"},{"key":"28_CR55","unstructured":"Fischer, V., Kumar, M.C., Metzen, J.H., Brox, T.: Adversarial examples for semantic image segmentation. In: International Conference on Learning Representations (ICLR) Workshop (2017)"},{"key":"28_CR56","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"28_CR57","unstructured":"Li, Y., Li, Y., Lv, Y., Jiang, Y., Xia, S.: Hidden backdoor attack against semantic segmentation models. CoRR, vol. abs\/2103.04038 (2021). arxiv:2103.04038"},{"key":"28_CR58","doi-asserted-by":"publisher","unstructured":"Agnihotri, S., Keuper, M.: Cospgd: a unified white-box adversarial attack for pixel-wise prediction tasks. CoRR, vol. abs\/2302.02213 (2023). https:\/\/doi.org\/10.48550\/arXiv.2302.02213","DOI":"10.48550\/arXiv.2302.02213"},{"key":"28_CR59","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhao, H., Jia, J.: Dynamic divide-and-conquer adversarial training for robust semantic segmentation. In: IEEE International Conference on Computer Vision (ICCV), pp. 7486\u20137495 (2021)","DOI":"10.1109\/ICCV48922.2021.00739"},{"key":"28_CR60","doi-asserted-by":"crossref","unstructured":"Xiao, C., Deng, R., Li, B., Yu, F., Liu, M., Song, D.: Characterizing adversarial examples based on spatial consistency information for semantic segmentation. In: European Conference on Computer Vision (ECCV), pp. 217\u2013234 (2018)","DOI":"10.1007\/978-3-030-01249-6_14"},{"key":"28_CR61","unstructured":"Lin, J., Song, C., He, K., Wang, L., Hopcroft, J.E.: Nesterov accelerated gradient and scale invariance for adversarial attacks. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"28_CR62","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Sun, J., Li, Z.: Rethinking adversarial transferability from a data distribution perspective. In: International Conference on Learning Representations (ICLR) (2022)","DOI":"10.1109\/TIP.2022.3211736"},{"key":"28_CR63","doi-asserted-by":"crossref","unstructured":"Xie, C., et al.: Improving transferability of adversarial examples with input diversity. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2730\u20132739 (2018)","DOI":"10.1109\/CVPR.2019.00284"},{"key":"28_CR64","doi-asserted-by":"crossref","unstructured":"Dong, Y., Pang, T., Su, H., Zhu, J.: Evading defenses to transferable adversarial examples by translation-invariant attacks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4312\u20134321 (2019)","DOI":"10.1109\/CVPR.2019.00444"},{"key":"28_CR65","unstructured":"Gu, J., Zhao, H., Tresp, V., Torr, P.H.S.: Adversarial examples on segmentation models can be easy to transfer. CoRR, vol. abs\/2111.11368 (2021). arxiv:2111.11368"},{"key":"28_CR66","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Boosting adversarial attacks with momentum. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9185\u20139193 (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"28_CR67","unstructured":"Nesterov, Y.E.: A method of solving a convex programming problem with convergence rate Doklady Akademii Nauk, vol. 269, no.\u00a03. Russian Academy of Sciences, pp. 543\u2013547 (1983)"},{"key":"28_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0041-5553(64)90137-5","volume":"4","author":"B Polyak","year":"1964","unstructured":"Polyak, B.: Some methods of speeding up the convergence of iteration methods. USSR Comput. Math. Math. Phys. 4, 1\u201317 (1964)","journal-title":"USSR Comput. Math. Math. Phys."},{"key":"28_CR69","doi-asserted-by":"crossref","unstructured":"Zheng, H., Yang, Z., Liu, W., Liang, J., Li, Y.: Improving deep neural networks using softplus units. In: International Joint Conference on Neural Networks (IJCNN), pp. 1\u20134. IEEE (2015)","DOI":"10.1109\/IJCNN.2015.7280459"},{"key":"28_CR70","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"28_CR71","unstructured":"Song, Y., Garg, S., Shi, J., Ermon, S.: Sliced score matching: a scalable approach to density and score estimation (2019). arxiv:1905.07088"},{"issue":"2","key":"28_CR72","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1137\/100802001","volume":"22","author":"Y Nesterov","year":"2012","unstructured":"Nesterov, Y.: Efficiency of coordinate descent methods on huge-scale optimization problems. SIAM J. Optim. 22(2), 341\u2013362 (2012)","journal-title":"SIAM J. Optim."},{"key":"28_CR73","unstructured":"Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. In: International Conference on Learning Representations (ICLR) (2017)"},{"issue":"2","key":"28_CR74","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010). https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int. J. Comput. Vis."},{"key":"28_CR75","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"issue":"4","key":"28_CR76","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. (TIP) 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"28_CR77","unstructured":"Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A.: Adversarial examples are not bugs, they are features. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 125\u2013136 (2019)"},{"key":"28_CR78","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["Lecture Notes in Computer Science","Databases Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1242-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T20:06:50Z","timestamp":1736194010000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1242-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819612413","9789819612420"],"references-count":78,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1242-0_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Database Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gold Coast, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adc-conference.github.io\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}