{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:33:56Z","timestamp":1774539236811,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62236009,U22A20103,6244160"],"award-info":[{"award-number":["62236009,U22A20103,6244160"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Distinguished Young Scholars","award":["62325603"],"award-info":[{"award-number":["62325603"]}]},{"name":"Beijing Natural Science Foundation for Distinguished Young Scholars","award":["JQ21015"],"award-info":[{"award-number":["JQ21015"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680639","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"2748-2756","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9972-2577","authenticated-orcid":false,"given":"Keming","family":"Wu","sequence":"first","affiliation":[{"name":"Chongqing University, Chongqing, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0904-8524","authenticated-orcid":false,"given":"Man","family":"Yao","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7788-7287","authenticated-orcid":false,"given":"Yuhong","family":"Chou","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8725-1619","authenticated-orcid":false,"given":"Xuerui","family":"Qiu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8337-1058","authenticated-orcid":false,"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"Software Security Technology Company Ltd, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1111-1529","authenticated-orcid":false,"given":"Bo","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8994-431X","authenticated-orcid":false,"given":"Guoqi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Machine Learning. PMLR, 274--283","author":"Athalye Anish","year":"2018","unstructured":"Anish Athalye, Nicholas Carlini, and David Wagner. 2018. Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In International Conference on Machine Learning. PMLR, 274--283."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. PMLR, 284--293","author":"Athalye Anish","year":"2018","unstructured":"Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. 2018. Synthesizing robust adversarial examples. In International Conference on Machine Learning. PMLR, 284--293."},{"key":"e_1_3_2_1_3_1","volume-title":"ESANN","volume":"48","author":"Bohte Sander M","year":"2000","unstructured":"Sander M Bohte, Joost N Kok, and Johannes A La Poutr\u00e9. 2000. SpikeProp: backpropagation for networks of spiking neurons.. In ESANN, Vol. 48. Bruges, 419--424."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00763"},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning. PMLR, 1310--1320","author":"Cohen Jeremy","year":"2019","unstructured":"Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. 2019. Certified adversarial robustness via randomized smoothing. In International Conference on Machine Learning. PMLR, 1310--1320."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.09.005"},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=_XNtisL32jv","author":"Deng Shikuang","year":"2022","unstructured":"Shikuang Deng, Yuhang Li, Shanghang Zhang, and Shi Gu. 2022. Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=_XNtisL32jv"},{"key":"e_1_3_2_1_9_1","first-page":"24780","article-title":"Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training","volume":"35","author":"Ding Jianhao","year":"2022","unstructured":"Jianhao Ding, Tong Bu, Zhaofei Yu, Tiejun Huang, and Jian Liu. 2022. Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training. Advances in Neural Information Processing Systems, Vol. 35 (2022), 24780--24793.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3008413"},{"key":"e_1_3_2_1_11_1","volume-title":"Neuronal dynamics: From single neurons to networks and models of cognition","author":"Gerstner Wulfram","unstructured":"Wulfram Gerstner, Werner M Kistler, Richard Naud, and Liam Paninski. 2014. Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press."},{"key":"e_1_3_2_1_12_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Goodfellow Ian J","year":"2015","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_13_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1952.sp004764"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00516"},{"key":"e_1_3_2_1_16_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Lee Guang-He","year":"2019","unstructured":"Guang-He Lee, Yang Yuan, Shiyu Chang, and Tommi Jaakkola. 2019. Tight certificates of adversarial robustness for randomly smoothed classifiers. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ITNT52450.2021.9649179"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"e_1_3_2_1_19_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_20_1","volume-title":"Science","volume":"345","author":"Merolla Paul A","year":"2014","unstructured":"Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, Andrew S Cassidy, Jun Sawada, Filipp Akopyan, Bryan L Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, et al. 2014. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, Vol. 345, 6197 (2014), 668--673."},{"key":"e_1_3_2_1_21_1","first-page":"3640","article-title":"Robustness of spiking neural networks based on time-to-first-spike encoding against adversarial attacks","volume":"69","author":"Nomura Osamu","year":"2022","unstructured":"Osamu Nomura, Yusuke Sakemi, Takeo Hosomi, and Takashi Morie. 2022. Robustness of spiking neural networks based on time-to-first-spike encoding against adversarial attacks. IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 69, 9 (2022), 3640--3644.","journal-title":"IEEE Transactions on Circuits and Systems II: Express Briefs"},{"key":"e_1_3_2_1_22_1","volume-title":"Nature","volume":"572","author":"Pei Jing","year":"2019","unstructured":"Jing Pei, Lei Deng, et al. 2019. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, Vol. 572, 7767 (2019), 106--111."},{"key":"e_1_3_2_1_23_1","volume-title":"Nature","volume":"572","author":"Pei Jing","year":"2019","unstructured":"Jing Pei, Lei Deng, Sen Song, Mingguo Zhao, Youhui Zhang, Shuang Wu, Guanrui Wang, Zhe Zou, Zhenzhi Wu, Wei He, et al. 2019. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, Vol. 572, 7767 (2019), 106--111."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00480-w"},{"key":"e_1_3_2_1_25_1","volume-title":"Nature","volume":"575","author":"Roy Kaushik","year":"2019","unstructured":"Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda. 2019. Towards spike-based machine intelligence with neuromorphic computing. Nature, Vol. 575, 7784 (2019), 607--617."},{"key":"e_1_3_2_1_26_1","volume-title":"Nature","volume":"575","author":"Roy Kaushik","year":"2019","unstructured":"Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda. 2019. Towards spike-based machine intelligence with neuromorphic computing. Nature, Vol. 575, 7784 (2019), 607--617."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851732"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_24"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_39"},{"key":"e_1_3_2_1_30_1","volume-title":"Robustness may be at odds with accuracy. arXiv preprint arXiv:1805.12152","author":"Tsipras Dimitris","year":"2018","unstructured":"Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. 2018. Robustness may be at odds with accuracy. arXiv preprint arXiv:1805.12152 (2018)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/1119971.1119974"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00331"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"e_1_3_2_1_34_1","volume-title":"International Conference on Machine Learning. PMLR, 10693--10705","author":"Yang Greg","year":"2020","unstructured":"Greg Yang, Tony Duan, J Edward Hu, Hadi Salman, Ilya Razenshteyn, and Jerry Li. 2020. Randomized smoothing of all shapes and sizes. In International Conference on Machine Learning. PMLR, 10693--10705."},{"key":"e_1_3_2_1_35_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Yao Man","year":"2024","unstructured":"Man Yao, JiaKui Hu, Tianxiang Hu, Yifan Xu, Zhaokun Zhou, Yonghong Tian, Bo XU, and Guoqi Li. 2024. Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_36_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Yao Man","year":"2024","unstructured":"Man Yao, Jiakui Hu, Zhaokun Zhou, Li Yuan, Yonghong Tian, Bo Xu, and Guoqi Li. 2024. Spike-driven transformer. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-47811-6"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3241201"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00397-w"},{"key":"e_1_3_2_1_40_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJx1Na4Fwr","author":"Zhai Runtian","year":"2020","unstructured":"Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, and Liwei Wang. 2020. MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJx1Na4Fwr"},{"key":"e_1_3_2_1_41_1","volume-title":"International Conference on Machine Learning. PMLR, 7472--7482","author":"Zhang Hongyang","year":"2019","unstructured":"Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan. 2019. Theoretically principled trade-off between robustness and accuracy. In International Conference on Machine Learning. PMLR, 7472--7482."},{"key":"e_1_3_2_1_42_1","volume-title":"Comment on\" Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network\". arXiv preprint arXiv:1907.00895","author":"Zimmermann Roland S","year":"2019","unstructured":"Roland S Zimmermann. 2019. Comment on\" Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network\". arXiv preprint arXiv:1907.00895 (2019)."}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680639","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680639","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:57Z","timestamp":1750295877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":42,"alternative-id":["10.1145\/3664647.3680639","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680639","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}