{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T12:16:11Z","timestamp":1732364171300,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,10]]},"DOI":"10.1145\/3665348.3665384","type":"proceedings-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T22:25:40Z","timestamp":1720045540000},"page":"206-210","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Robustness under New Adversarial Threats: A Comprehensive Analysis of Deep Neural Network Defenses"],"prefix":"10.1145","author":[{"ORCID":"http:\/\/orcid.org\/0009-0009-1906-1202","authenticated-orcid":false,"given":"Wenzhao","family":"Liu","sequence":"first","affiliation":[{"name":"PLA Strategy Support Force Information Engineering University, China and The PLA Unit 63892, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0201-9229","authenticated-orcid":false,"given":"Kuiwu","family":"Yang","sequence":"additional","affiliation":[{"name":"PLA Strategy Support Force Information Engineering University, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2323-7820","authenticated-orcid":false,"given":"Yue","family":"Chen","sequence":"additional","affiliation":[{"name":"PLA Strategy Support Force Information Engineering University, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0002-5797-6584","authenticated-orcid":false,"given":"Huanyao","family":"Dai","sequence":"additional","affiliation":[{"name":"The PLA Unit 63892, China"}]}],"member":"320","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Max Kaufmann Daniel Kang Yi Sun Steven Basart Xuwang Yin Mantas Mazeika Akul Arora Adam Dziedzic Franziska Boenisch Tom Brown Jacob Steinhardt and Dan Hendrycks. 2019. Testing Robustness Against Unforeseen Adversaries. arXiv:1908.08016. Retrieved from http:\/\/arxiv.org\/abs\/1908.08016."},{"key":"e_1_3_2_1_2_1","unstructured":"Jiashuo Liu Zheyan Shen Yue He Xingxuan Zhang Renzhe Xu Han Yu and Peng Cui. 2021. Towards Out-Of-Distribution Generalization: A Survey. arXiv:2108.13624. Retrieved from http:\/\/arxiv.org\/abs\/2108.13624."},{"volume-title":"Proceedings of the 2nd International Conference on Learning Representations (ICLR'14)","year":"2014","author":"Szegedy Christian","key":"e_1_3_2_1_3_1","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In Proceedings of the 2nd International Conference on Learning Representations (ICLR'14)."},{"volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR'18)","year":"2018","author":"Madry Aleksander","key":"e_1_3_2_1_4_1","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In Proceedings of the 6th International Conference on Learning Representations (ICLR'18)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3495973"},{"volume-title":"Proceedings of the 37th International Conference on Machine Learning (ICML'20)","author":"Rice Leslie","key":"e_1_3_2_1_6_1","unstructured":"Leslie Rice, Eric Wong, and J. Zico Kolter. 2020. Overfitting in adversarially robust deep learning. In Proceedings of the 37th International Conference on Machine Learning (ICML'20). ACM Inc., New York, NY, 8093\u20138104."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327409"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455221"},{"volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR'18)","year":"2018","author":"Xiao Chaowei","key":"e_1_3_2_1_9_1","unstructured":"Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song. 2018. Spatially Transformed Adversarial Examples. In Proceedings of the 6th International Conference on Learning Representations (ICLR'18)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00023"},{"key":"e_1_3_2_1_11_1","unstructured":"Nicolas Papernot Fartash Faghri Nicholas Carlini Ian Goodfellow Reuben Feinman Alexey Kurakin Cihang Xie Yash Sharma Tom Brown Aurko Roy Alexander Matyasko Vahid Behzadan Karen Hambardzumyan Zhishuai Zhang Yi-Lin Juang Zhi Li Ryan Sheatsley Abhibhav Garg Jonathan Uesato Willi Gierke Yinpeng Dong David Berthelot Paul Hendricks Jonas Rauber Rujun Long and Patrick McDaniel. 2018. Technical Report on the CleverHans v2.1.0 Adversarial Examples Library. arXiv:1610.00768. Retrieved from http:\/\/arxiv.org\/abs\/1610.00768."},{"volume-title":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks","year":"2021","author":"Croce Francesco","key":"e_1_3_2_1_12_1","unstructured":"Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein. 2021. RobustBench: a standardized adversarial robustness benchmark. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021). Curran Associates Inc., Red Hook, NY, USA."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Shiyu Tang Ruihao Gong Yan Wang Aishan Liu Jiakai Wang Xinyun Chen Fengwei Yu Xianglong Liu Dawn Song Alan Yuille Philip H. S. Torr and Dacheng Tao. 2021. RobustART: Benchmarking Robustness on Architecture Design and Training Techniques. arXiv:2109.05211. Retrieved from http:\/\/arxiv.org\/abs\/2109.05211.","DOI":"10.1364\/AO.412676"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Chang Liu Yinpeng Dong Wenzhao Xiang Xiao Yang Hang Su Jun Zhu Yuefeng Chen Yuan He Hui Xue and Shibao Zheng. 2023. A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking. arXiv:2302.14301. Retrieved from http:\/\/arxiv.org\/abs\/2302.14301.","DOI":"10.1007\/s11263-024-02196-3"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022"},{"volume-title":"Proceedings of the 40th International Conference on Machine Learning (ICML'23)","year":"2023","author":"Dai Sihui","key":"e_1_3_2_1_16_1","unstructured":"Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, and Prateek Mittal. 2023. MultiRobustBench: benchmarking robustness against multiple attacks. In Proceedings of the 40th International Conference on Machine Learning (ICML'23). ACM Inc., New York, NY, 6760\u20136785."},{"volume-title":"Foolbox: A Python toolbox to benchmark the robustness of machine learning models. arXiv:1707.04131.","year":"2017","author":"Rauber Jonas","key":"e_1_3_2_1_17_1","unstructured":"Jonas Rauber, Wieland Brendel, and Matthias Bethge. 2017. Foolbox: A Python toolbox to benchmark the robustness of machine learning models. arXiv:1707.04131. Retrieved from http:\/\/arxiv.org\/abs\/1707.04131."},{"volume-title":"Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, and Ben Edwards.","year":"2018","author":"Nicolae Maria-Irina","key":"e_1_3_2_1_18_1","unstructured":"Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, and Ben Edwards. 2018. Adversarial Robustness Toolbox v1.0.0. arXiv: 1807.01069. Retrieved from http:\/\/arxiv.org\/abs\/1807.01069."},{"volume-title":"Proceedings of the 9th International Conference on Learning Representations (ICLR'21)","year":"2021","author":"Laidlaw Cassidy","key":"e_1_3_2_1_19_1","unstructured":"Cassidy Laidlaw, Sahil Singla, and Soheil Feizi. 2021. Perceptual Adversarial Robustness: Defense Against Unseen Threat Models. In Proceedings of the 9th International Conference on Learning Representations (ICLR'21)."},{"volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML'19)","year":"2019","author":"Engstrom Logan","key":"e_1_3_2_1_20_1","unstructured":"Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry. 2019. Exploring the Landscape of Spatial Robustness. In Proceedings of the 36th International Conference on Machine Learning (ICML'19). ACM Inc., New York, NY, 1802\u20131811."},{"volume-title":"Proceedings of the 35th International Conference on Machine Learning (ICML'18)","year":"2018","author":"Karmon Danny","key":"e_1_3_2_1_21_1","unstructured":"Danny Karmon, Daniel Zoran, and Yoav Goldberg. 2018. LaVAN: Localized and Visible Adversarial Noise. In Proceedings of the 35th International Conference on Machine Learning (ICML'18). ACM Inc., New York, NY, 2507\u20132515."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3600270.3600899"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02362"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/3618408.3619915"}],"event":{"name":"GAIIS 2024: 2024 International Conference on Generative Artificial Intelligence and Information Security","acronym":"GAIIS 2024","location":"Kuala Lumpur Malaysia"},"container-title":["Proceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3665348.3665384","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T11:39:49Z","timestamp":1732361989000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3665348.3665384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,10]]},"references-count":24,"alternative-id":["10.1145\/3665348.3665384","10.1145\/3665348"],"URL":"https:\/\/doi.org\/10.1145\/3665348.3665384","relation":{},"subject":[],"published":{"date-parts":[[2024,5,10]]},"assertion":[{"value":"2024-07-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}