{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:59:04Z","timestamp":1780354744809,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":45,"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":"Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies, and Ant Group"},{"name":"the NSFC","award":["No. 62272411"],"award-info":[{"award-number":["No. 62272411"]}]},{"name":"the Zhejiang NSF","award":["LR21F020004"],"award-info":[{"award-number":["LR21F020004"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3611953","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:27:40Z","timestamp":1698391660000},"page":"3796-3806","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4859-1757","authenticated-orcid":false,"given":"Dong","family":"Chen","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7356-9711","authenticated-orcid":false,"given":"Siliang","family":"Tang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3540-7997","authenticated-orcid":false,"given":"Zijin","family":"Shen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3131-6916","authenticated-orcid":false,"given":"Guoming","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6142-9914","authenticated-orcid":false,"given":"Jun","family":"Xiao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9017-2508","authenticated-orcid":false,"given":"Yueting","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9145-4531","authenticated-orcid":false,"given":"Carl","family":"Yang","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Toward on-device federated learning: a direct acyclic graph-based blockchain approach","author":"Cao Mingrui","year":"2021","unstructured":"Mingrui Cao, Long Zhang, and Bin Cao. 2021. Toward on-device federated learning: a direct acyclic graph-based blockchain approach. IEEE Transactions on Neural Networks and Learning Systems (2021)."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. PMLR, 854--863","author":"Cisse Moustapha","year":"2017","unstructured":"Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier. 2017. Parseval networks: Improving robustness to adversarial examples. In International Conference on Machine Learning. PMLR, 854--863."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2015.2389413"},{"key":"e_1_3_2_1_4_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_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3105723"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3116976"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_1_8_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Machine Learning. PMLR, 4387--4398","author":"Hsieh Kevin","year":"2020","unstructured":"Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons. 2020. The non-iid data quagmire of decentralized machine learning. In International Conference on Machine Learning. PMLR, 4387--4398."},{"key":"e_1_3_2_1_10_1","volume-title":"Adversarial examples are not bugs, they are features. arXiv preprint arXiv:1905.02175","author":"Ilyas Andrew","year":"2019","unstructured":"Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. 2019. Adversarial examples are not bugs, they are features. arXiv preprint arXiv:1905.02175 (2019)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152581"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20216230"},{"key":"e_1_3_2_1_13_1","unstructured":"Alex Krizhevsky Geoffrey Hinton et al. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"e_1_3_2_1_15_1","volume-title":"Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.","author":"Li Tian","year":"2018","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2018a. Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018)."},{"key":"e_1_3_2_1_16_1","volume-title":"Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.","author":"Li Tian","year":"2018","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2018b. Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018)."},{"key":"e_1_3_2_1_17_1","unstructured":"Xiaoxiao Li Meirui Jiang Xiaofei Zhang Michael Kamp and Qi Dou. 2021b. FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. arxiv: cs.LG\/2102.07623"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.120291"},{"key":"e_1_3_2_1_19_1","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume":"33","author":"Lin Tao","year":"2020","unstructured":"Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. 2020. Ensemble distillation for robust model fusion in federated learning. Advances in Neural Information Processing Systems, Vol. 33 (2020), 2351--2363.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_2_1_21_1","unstructured":"Alireza Makhzani Jonathon Shlens Navdeep Jaitly Ian Goodfellow and Brendan Frey. 2016. Adversarial Autoencoders. arxiv: cs.LG\/1511.05644"},{"key":"e_1_3_2_1_22_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics. PMLR 1273--1282."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"e_1_3_2_1_24_1","volume-title":"Federated adversarial domain adaptation. arXiv preprint arXiv:1911.02054","author":"Peng Xingchao","year":"2019","unstructured":"Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko. 2019. Federated adversarial domain adaptation. arXiv preprint arXiv:1911.02054 (2019)."},{"key":"e_1_3_2_1_25_1","volume-title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints","author":"Sattler Felix","year":"2020","unstructured":"Felix Sattler, Klaus-Robert M\u00fcller, and Wojciech Samek. 2020. Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints. IEEE transactions on neural networks and learning systems, Vol. 32, 8 (2020), 3710--3722."},{"key":"e_1_3_2_1_26_1","volume-title":"Robust and communication-efficient federated learning from non-iid data","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler, Simon Wiedemann, Klaus-Robert M\u00fcller, and Wojciech Samek. 2019. Robust and communication-efficient federated learning from non-iid data. IEEE transactions on neural networks and learning systems, Vol. 31, 9 (2019), 3400--3413."},{"key":"e_1_3_2_1_27_1","volume-title":"12th {USENIX} Workshop on Offensive Technologies ({WOOT} 18).","author":"Song Dawn","unstructured":"Dawn Song, Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramer, Atul Prakash, and Tadayoshi Kohno. 2018. Physical adversarial examples for object detectors. In 12th {USENIX} Workshop on Offensive Technologies ({WOOT} 18)."},{"key":"e_1_3_2_1_28_1","volume-title":"Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199","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. arXiv preprint arXiv:1312.6199 (2013)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"e_1_3_2_1_30_1","volume-title":"Attention is all you need. arXiv preprint arXiv:1706.03762","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)."},{"key":"e_1_3_2_1_31_1","volume-title":"Generalizing to unseen domains via adversarial data augmentation. arXiv preprint arXiv:1805.12018","author":"Volpi Riccardo","year":"2018","unstructured":"Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, and Silvio Savarese. 2018. Generalizing to unseen domains via adversarial data augmentation. arXiv preprint arXiv:1805.12018 (2018)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2960507"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2589879"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3056991"},{"key":"e_1_3_2_1_35_1","volume-title":"Generating adversarial examples with adversarial networks. arXiv preprint arXiv:1801.02610","author":"Xiao Chaowei","year":"2018","unstructured":"Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song. 2018. Generating adversarial examples with adversarial networks. arXiv preprint arXiv:1801.02610 (2018)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00090"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3234278"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3067449"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3339474"},{"key":"e_1_3_2_1_40_1","volume-title":"Adversarial examples: Opportunities and challenges","author":"Zhang Jiliang","year":"2019","unstructured":"Jiliang Zhang and Chen Li. 2019. Adversarial examples: Opportunities and challenges. IEEE transactions on neural networks and learning systems, Vol. 31, 7 (2019), 2578--2593."},{"key":"e_1_3_2_1_41_1","volume-title":"Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks","author":"Zhang Jiawei","year":"2022","unstructured":"Jiawei Zhang, Jinwei Wang, Hao Wang, and Xiangyang Luo. 2022. Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks. IEEE Transactions on Circuits and Systems for Video Technology (2022)."},{"key":"e_1_3_2_1_42_1","volume-title":"Visual Content Privacy Protection: A Survey. arXiv preprint arXiv:2303.16552","author":"Zhao Ruoyu","year":"2023","unstructured":"Ruoyu Zhao, Yushu Zhang, Tao Wang, Wenying Wen, Yong Xiang, and Xiaochun Cao. 2023. Visual Content Privacy Protection: A Survey. arXiv preprint arXiv:2303.16552 (2023)."},{"key":"e_1_3_2_1_43_1","volume-title":"Federated learning with non-iid data. arXiv preprint arXiv:1806.00582","author":"Zhao Yue","year":"2018","unstructured":"Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)."},{"key":"e_1_3_2_1_44_1","volume-title":"Only Once Attack: Fooling the Tracker with Adversarial Template","author":"Zhou Ze","year":"2023","unstructured":"Ze Zhou, Yinghui Sun, Quansen Sun, Chaobo Li, and Zhenwen Ren. 2023. Only Once Attack: Fooling the Tracker with Adversarial Template. IEEE Transactions on Circuits and Systems for Video Technology (2023)."},{"key":"e_1_3_2_1_45_1","volume-title":"Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on mathematical software (TOMS)","author":"Zhu Ciyou","year":"1997","unstructured":"Ciyou Zhu, Richard H Byrd, Peihuang Lu, and Jorge Nocedal. 1997. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on mathematical software (TOMS), Vol. 23, 4 (1997), 550--560."}],"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\/3581783.3611953","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3611953","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:04:58Z","timestamp":1755821098000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3611953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":45,"alternative-id":["10.1145\/3581783.3611953","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3611953","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"}}]}}