{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:16:20Z","timestamp":1772766980291,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":62,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Key R\\&D Program of China","award":["2022YFA1004101"],"award-info":[{"award-number":["2022YFA1004101"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3612164","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:26:54Z","timestamp":1698391614000},"page":"8185-8194","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1032-169X","authenticated-orcid":false,"given":"Xianghao","family":"Jiao","sequence":"first","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9057-1645","authenticated-orcid":false,"given":"Yaohua","family":"Liu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0023-1269","authenticated-orcid":false,"given":"Jiaxin","family":"Gao","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0773-7606","authenticated-orcid":false,"given":"Xinyuan","family":"Chu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8991-4188","authenticated-orcid":false,"given":"Xin","family":"Fan","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9554-0565","authenticated-orcid":false,"given":"Risheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks. arXiv preprint arXiv:2302.02213","author":"Agnihotri Shashank","year":"2023","unstructured":"Shashank Agnihotri and Margret Keuper. 2023. CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks. arXiv preprint arXiv:2302.02213 (2023)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00099"},{"key":"e_1_3_2_1_3_1","volume-title":"On the robustness of the cvpr 2018 white-box adversarial example defenses. arXiv preprint arXiv:1804.03286","author":"Athalye Anish","year":"2018","unstructured":"Anish Athalye and Nicholas Carlini. 2018. On the robustness of the cvpr 2018 white-box adversarial example defenses. arXiv preprint arXiv:1804.03286 (2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00178"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In 2017 ieee symposium on security and privacy (sp). Ieee 39--57.","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_6_1","volume-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","author":"Chen Liang-Chieh","year":"2017","unstructured":"Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40, 4 (2017), 834--848."},{"key":"e_1_3_2_1_7_1","volume-title":"Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587","author":"Chen Liang-Chieh","year":"2017","unstructured":"Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"e_1_3_2_1_10_1","volume-title":"International conference on machine learning. PMLR, 2206--2216","author":"Croce Francesco","year":"2020","unstructured":"Francesco Croce and Matthias Hein. 2020. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning. PMLR, 2206--2216."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01457"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"e_1_3_2_1_13_1","volume-title":"Christopher KI Williams, John Winn, and Andrew Zisserman.","author":"Everingham Mark","year":"2010","unstructured":"Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. 2010. The pascal visual object classes (voc) challenge. International journal of computer vision 88 (2010), 303--338."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.186"},{"key":"e_1_3_2_1_15_1","volume-title":"Lightweight pyramid networks for image deraining","author":"Fu Xueyang","year":"2019","unstructured":"Xueyang Fu, Borong Liang, Yue Huang, Xinghao Ding, and John Paisley. 2019. Lightweight pyramid networks for image deraining. IEEE transactions on neural networks and learning systems 31, 6 (2019), 1794--1807."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20102962"},{"key":"e_1_3_2_1_17_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_3_2_1_18_1","volume-title":"Tel Aviv","author":"Gu Jindong","year":"2022","unstructured":"Jindong Gu, Hengshuang Zhao, Volker Tresp, and Philip HS Torr. 2022. SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXIX. Springer, 308--325."},{"key":"e_1_3_2_1_19_1","volume-title":"Towards deep neural network architectures robust to adversarial examples. arXiv preprint arXiv:1412.5068","author":"Gu Shixiang","year":"2014","unstructured":"Shixiang Gu and Luca Rigazio. 2014. Towards deep neural network architectures robust to adversarial examples. arXiv preprint arXiv:1412.5068 (2014)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.300"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00821"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00837"},{"key":"e_1_3_2_1_24_1","volume-title":"Automatic single-imagebased rain streaks removal via image decomposition","author":"Kang Li-Wei","year":"2011","unstructured":"Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu. 2011. Automatic single-imagebased rain streaks removal via image decomposition. IEEE transactions on image processing 21, 4 (2011), 1742--1755."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548362"},{"key":"e_1_3_2_1_26_1","volume-title":"Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236","author":"Kurakin Alexey","year":"2016","unstructured":"Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016. Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)."},{"key":"e_1_3_2_1_27_1","volume-title":"Artificial intelligence safety and security","author":"Kurakin Alexey","unstructured":"Alexey Kurakin, Ian J Goodfellow, and Samy Bengio. 2018. Adversarial examples in the physical world. In Artificial intelligence safety and security. Chapman and Hall\/CRC, 99--112."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.34"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00173"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00396"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_16"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00191"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6831"},{"key":"e_1_3_2_1_34_1","volume-title":"Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083","author":"Madry Aleksander","year":"2017","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_10"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00894"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00406"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00299"},{"key":"e_1_3_2_1_40_1","volume-title":"Improving the generalization of adversarial training with domain adaptation. arXiv preprint arXiv:1810.00740","author":"Song Chuanbiao","year":"2018","unstructured":"Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft. 2018. Improving the generalization of adversarial training with domain adaptation. arXiv preprint arXiv:1810.00740 (2018)."},{"key":"e_1_3_2_1_41_1","volume-title":"Pixeldefend: Leveraging generative models to understand and defend against adversarial examples. arXiv preprint arXiv:1710.10766","author":"Song Yang","year":"2017","unstructured":"Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman. 2017. Pixeldefend: Leveraging generative models to understand and defend against adversarial examples. arXiv preprint arXiv:1710.10766 (2017)."},{"key":"e_1_3_2_1_42_1","first-page":"4461","article-title":"Rethinking Image Restoration for Object Detection","volume":"35","author":"Sun Shangquan","year":"2022","unstructured":"Shangquan Sun, Wenqi Ren, Tao Wang, and Xiaochun Cao. 2022. Rethinking Image Restoration for Object Detection. Advances in Neural Information Processing Systems 35 (2022), 4461--4474.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_43_1","volume-title":"Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:1705.07204","author":"Tram\u00e8r Florian","year":"2017","unstructured":"Florian Tram\u00e8r, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. 2017. Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:1705.07204 (2017)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00239"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME51207.2021.9428187"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547989"},{"key":"e_1_3_2_1_47_1","volume-title":"Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994","author":"Wong Eric","year":"2020","unstructured":"Eric Wong, Leslie Rice, and J Zico Kolter. 2020. Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994 (2020)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_14"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.153"},{"key":"e_1_3_2_1_50_1","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie Enze","year":"2021","unstructured":"Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems 34 (2021), 12077--12090.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3116794"},{"key":"e_1_3_2_1_52_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision. 7486--7495","author":"Xu Xiaogang","year":"2021","unstructured":"Xiaogang Xu, Hengshuang Zhao, and Jiaya Jia. 2021. Dynamic divide-andconquer adversarial training for robust semantic segmentation. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 7486--7495."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00388"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01243"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00747"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00079"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3547801"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_5"},{"key":"e_1_3_2_1_60_1","volume-title":"International Conference on Machine Learning. PMLR, 26693--26712","author":"Zhang Yihua","year":"2022","unstructured":"Yihua Zhang, Guanhua Zhang, Prashant Khanduri, Mingyi Hong, Shiyu Chang, and Sijia Liu. 2022. Revisiting and advancing fast adversarial training through the lens of bi-level optimization. In International Conference on Machine Learning. PMLR, 26693--26712."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"e_1_3_2_1_62_1","volume-title":"AGLNet: Towards real-time semantic segmentation of self-driving images via attention-guided lightweight network. applied soft computing 96","author":"Zhou Quan","year":"2020","unstructured":"Quan Zhou, Yu Wang, Yawen Fan, Xiaofu Wu, Suofei Zhang, Bin Kang, and Longin Jan Latecki. 2020. AGLNet: Towards real-time semantic segmentation of self-driving images via attention-guided lightweight network. applied soft computing 96 (2020), 106682."}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","location":"Ottawa ON Canada","acronym":"MM '23","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612164","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3612164","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:05:43Z","timestamp":1755821143000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3612164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":62,"alternative-id":["10.1145\/3581783.3612164","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3612164","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}