{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:37:23Z","timestamp":1781584643656,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":66,"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\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021ZD0112804"],"award-info":[{"award-number":["2021ZD0112804"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276067"],"award-info":[{"award-number":["62276067"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3681032","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:33Z","timestamp":1729925973000},"page":"6212-6221","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9531-6662","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"first","affiliation":[{"name":"Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5195-6107","authenticated-orcid":false,"given":"Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2099-4973","authenticated-orcid":false,"given":"Xingjun","family":"Ma","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1543-6889","authenticated-orcid":false,"given":"Zhineng","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-264X","authenticated-orcid":false,"given":"Jingjing","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1907-8567","authenticated-orcid":false,"given":"Yu-Gang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_29"},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Learning Representations.","author":"Bai Yang","year":"2021","unstructured":"Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, and Yisen Wang. 2021. Improving Adversarial Robustness via Channel-wise Activation Suppressing. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_3_1","volume-title":"Clip Retrieval: Easily compute clip embeddings and build a clip retrieval system with them. https:\/\/github.com\/rom1504\/clip-retrieval.","author":"Beaumont Romain","year":"2022","unstructured":"Romain Beaumont. 2022. Clip Retrieval: Easily compute clip embeddings and build a clip retrieval system with them. https:\/\/github.com\/rom1504\/clip-retrieval."},{"key":"e_1_3_2_1_4_1","volume-title":"Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. In International Conference on Learning Representations.","author":"Brendel Wieland","year":"2018","unstructured":"Wieland Brendel, Jonas Rauber, and Matthias Bethge. 2018. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_5_1","volume-title":"Adversarial objects against lidar-based autonomous driving systems. arXiv preprint arXiv:1907.05418","author":"Cao Yulong","year":"2019","unstructured":"Yulong Cao, Chaowei Xiao, Dawei Yang, Jing Fang, Ruigang Yang, Mingyan Liu, and Bo Li. 2019. Adversarial objects against lidar-based autonomous driving systems. arXiv preprint arXiv:1907.05418 (2019)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403225"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00045"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612110"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140448"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3385003.3410925"},{"key":"e_1_3_2_1_11_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_1_12_1","volume-title":"Sign-OPT: A Query-Efficient Hard-label Adversarial Attack. In International Conference on Learning Representations.","author":"Cheng Minhao","year":"2020","unstructured":"Minhao Cheng, Simranjit Singh, Patrick H Chen, Pin-Yu Chen, Sijia Liu, and Cho-Jui Hsieh. 2020. Sign-OPT: A Query-Efficient Hard-label Adversarial Attack. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Learning Representations. International Conference on Learning Representations, ICLR.","author":"Cheng Minhao","year":"2019","unstructured":"Minhao Cheng, Huan Zhang, Cho-Jui Hsieh, Thong Le, Pin-Yu Chen, and Jinfeng Yi. 2019. Query-efficient hard-label black-box attack: An optimization-based approach. In International Conference on Learning Representations. International Conference on Learning Representations, ICLR."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.04.005"},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Machine Learning.","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."},{"key":"e_1_3_2_1_16_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"e_1_3_2_1_18_1","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2010. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv 2020. arXiv preprint arXiv:2010.11929 (2010)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Kevin Eykholt Ivan Evtimov Earlence Fernandes Bo Li Amir Rahmati Chaowei Xiao Atul Prakash Tadayoshi Kohno and Dawn Song. 2018. Robust physical-world attacks on deep learning visual classification. In CVPR. 1625--1634.","DOI":"10.1109\/CVPR.2018.00175"},{"key":"e_1_3_2_1_20_1","volume-title":"When does contrastive learning preserve adversarial robustness from pretraining to finetuning? Advances in neural information processing systems","author":"Fan Lijie","year":"2021","unstructured":"Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, and Chuang Gan. 2021. When does contrastive learning preserve adversarial robustness from pretraining to finetuning? Advances in neural information processing systems, Vol. 34 (2021), 21480--21492."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3623116"},{"key":"e_1_3_2_1_22_1","volume-title":"Andrew L Beam, and Isaac S Kohane","author":"Finlayson Samuel G","year":"2019","unstructured":"Samuel G Finlayson, John D Bowers, Joichi Ito, Jonathan L Zittrain, Andrew L Beam, and Isaac S Kohane. 2019. Adversarial attacks on medical machine learning. Science, Vol. 363, 6433 (2019), 1287--1289."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_25_1","first-page":"17081","article-title":"Contrastive learning with adversarial examples","volume":"33","author":"Ho Chih-Hui","year":"2020","unstructured":"Chih-Hui Ho and Nuno Nvasconcelos. 2020. Contrastive learning with adversarial examples. Advances in Neural Information Processing Systems, Vol. 33 (2020), 17081--17093.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_26_1","volume-title":"Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks. arXiv preprint arXiv:2307.16331","author":"Hooda Ashish","year":"2023","unstructured":"Ashish Hooda, Neal Mangaokar, Ryan Feng, Kassem Fawaz, Somesh Jha, and Atul Prakash. 2023. Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks. arXiv preprint arXiv:2307.16331 (2023)."},{"key":"e_1_3_2_1_27_1","volume-title":"International conference on machine learning. PMLR, 2137--2146","author":"Ilyas Andrew","year":"2018","unstructured":"Andrew Ilyas, Logan Engstrom, Anish Athalye, and Jessy Lin. 2018. Black-box adversarial attacks with limited queries and information. In International conference on machine learning. PMLR, 2137--2146."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3351088"},{"key":"e_1_3_2_1_29_1","volume-title":"Robust pre-training by adversarial contrastive learning. Advances in neural information processing systems","author":"Jiang Ziyu","year":"2020","unstructured":"Ziyu Jiang, Tianlong Chen, Ting Chen, and Zhangyang Wang. 2020. Robust pre-training by adversarial contrastive learning. Advances in neural information processing systems, Vol. 33 (2020), 16199--16210."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2019.00044"},{"key":"e_1_3_2_1_32_1","first-page":"2983","article-title":"Adversarial self-supervised contrastive learning","volume":"33","author":"Kim Minseon","year":"2020","unstructured":"Minseon Kim, Jihoon Tack, and Sung Ju Hwang. 2020. Adversarial self-supervised contrastive learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 2983--2994.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_33_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_34_1","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Li Huiying","year":"2022","unstructured":"Huiying Li, Shawn Shan, Emily Wenger, Jiayun Zhang, Haitao Zheng, and Ben Y Zhao. 2022. Blacklight: Scalable defense for neural networks against Query-BasedBlack-Box attacks. In 31st USENIX Security Symposium (USENIX Security 22). 2117--2134."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00130"},{"key":"e_1_3_2_1_36_1","volume-title":"Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning. In The Eleventh International Conference on Learning Representations.","author":"Luo Rundong","year":"2023","unstructured":"Rundong Luo, Yifei Wang, and Yisen Wang. 2023. Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-023-06328-7"},{"key":"e_1_3_2_1_38_1","volume-title":"Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. In International Conference on Learning Representations.","author":"Ma Xingjun","year":"2018","unstructured":"Xingjun Ma, Bo Li, Yisen Wang, Sarah M Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Dawn Song, Michael E Houle, and James Bailey. 2018. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107332"},{"key":"e_1_3_2_1_40_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_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01029"},{"key":"e_1_3_2_1_42_1","volume-title":"Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian Molloy, and Ben Edwards.","author":"Nicolae Maria-Irina","year":"2018","unstructured":"Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian Molloy, and Ben Edwards. 2018. Adversarial Robustness Toolbox v1.2.0. CoRR, Vol. 1807.01069 (2018). https:\/\/arxiv.org\/pdf\/1807.01069"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"e_1_3_2_1_44_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_1_45_1","volume-title":"View in Article","volume":"2","author":"R","year":"2023","unstructured":"R OpenAI. 2023. Gpt-4 technical report. arxiv 2303.08774. View in Article, Vol. 2, 5 (2023)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"e_1_3_2_1_47_1","volume-title":"Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al.","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In ICML."},{"key":"e_1_3_2_1_48_1","volume-title":"Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125","author":"Ramesh Aditya","year":"2022","unstructured":"Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (2022)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael Bernstein et al. 2015. Imagenet large scale visual recognition challenge. International journal of computer vision Vol. 115 (2015) 211--252.","DOI":"10.1007\/s11263-015-0816-y"},{"key":"e_1_3_2_1_50_1","volume-title":"The cost of training nlp models: A concise overview. arXiv preprint arXiv:2004.08900","author":"Sharir Or","year":"2020","unstructured":"Or Sharir, Barak Peleg, and Yoav Shoham. 2020. The cost of training nlp models: A concise overview. arXiv preprint arXiv:2004.08900 (2020)."},{"key":"e_1_3_2_1_51_1","volume-title":"Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural networks","author":"Stallkamp Johannes","year":"2012","unstructured":"Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. 2012. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural networks, Vol. 32 (2012), 323--332."},{"key":"e_1_3_2_1_52_1","volume-title":"International Conference on Learning Representations.","author":"Szegedy Christian","year":"2013","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013. Intriguing properties of neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2023.3304476"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301742"},{"key":"e_1_3_2_1_55_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_56_1","volume-title":"On the Convergence and Robustness of Adversarial Training. In International Conference on Machine Learning.","author":"Wang Yisen","year":"2019","unstructured":"Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu. 2019. On the Convergence and Robustness of Adversarial Training. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_57_1","volume-title":"International conference on learning representations.","author":"Wang Yisen","year":"2020","unstructured":"Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu. 2020. Improving adversarial robustness requires revisiting misclassified examples. In International conference on learning representations."},{"key":"e_1_3_2_1_58_1","volume-title":"Adaptive Cross-Modal Transferable Adversarial Attacks From Images to Videos","author":"Wei Zhipeng","year":"2023","unstructured":"Zhipeng Wei, Jingjing Chen, Zuxuan Wu, and Yu-Gang Jiang. 2023. Adaptive Cross-Modal Transferable Adversarial Attacks From Images to Videos. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)."},{"key":"e_1_3_2_1_59_1","volume-title":"Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets. In International Conference on Learning Representations.","author":"Wu Dongxian","year":"2020","unstructured":"Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, and Xingjun Ma. 2020. Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_60_1","volume-title":"Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155","author":"Xu Weilin","year":"2017","unstructured":"Weilin Xu, David Evans, and Yanjun Qi. 2017. Feature squeezing: Detecting adversarial examples in deep neural networks. arXiv preprint arXiv:1704.01155 (2017)."},{"key":"e_1_3_2_1_61_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Xu Xilie","year":"2024","unstructured":"Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, and Mohan S Kankanhalli. 2024. Efficient adversarial contrastive learning via robustness-aware coreset selection. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_62_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Xu Xilie","year":"2024","unstructured":"Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, and Mohan S Kankanhalli. 2024. Enhancing adversarial contrastive learning via adversarial invariant regularization. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_63_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_64_1","doi-asserted-by":"crossref","unstructured":"Jiaming Zhang Xingjun Ma Xin Wang Lingyu Qiu Jiaqi Wang Yu-Gang Jiang and Jitao Sang. 2023. Adversarial prompt tuning for vision-language models. arXiv preprint arXiv:2311.11261.","DOI":"10.1007\/978-3-031-72995-9_4"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474369.3486863"},{"key":"e_1_3_2_1_66_1","volume-title":"Ngai-Man Man Cheung, and Min Lin","author":"Zhao Yunqing","year":"2024","unstructured":"Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Man Cheung, and Min Lin. 2024. On evaluating adversarial robustness of large vision-language models. Advances in Neural Information Processing Systems, Vol. 36 (2024)."}],"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\/3664647.3681032","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3681032","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:37Z","timestamp":1750295857000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":66,"alternative-id":["10.1145\/3664647.3681032","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3681032","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"}}]}}