{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:40:26Z","timestamp":1755823226458,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF-17-1-0356"],"award-info":[{"award-number":["W911NF-17-1-0356"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["OAC-1828467, DMS-1925346, CNS-2029661, OAC-2115094"],"award-info":[{"award-number":["OAC-1828467, DMS-1925346, CNS-2029661, OAC-2115094"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,15]]},"DOI":"10.1145\/3576915.3623177","type":"proceedings-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T12:35:13Z","timestamp":1700570113000},"page":"801-814","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Attack Some while Protecting Others: Selective Attack Strategies for Attacking and Protecting Multiple Concepts"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1244-7702","authenticated-orcid":false,"given":"Vibha","family":"Belavadi","sequence":"first","affiliation":[{"name":"University of Texas at Dallas, Richardson, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2134-2531","authenticated-orcid":false,"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Texas at Dallas, Richardson, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9795-9063","authenticated-orcid":false,"given":"Murat","family":"Kantarcioglu","sequence":"additional","affiliation":[{"name":"University of Texas at Dallas, Richardson, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4653-2080","authenticated-orcid":false,"given":"Bhavani","family":"Thuraisingham","sequence":"additional","affiliation":[{"name":"University of Texas Dallas, Richardson, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"volume-title":"Social Computing, Behavioral-Cultural Modeling","author":"Collins Susan","key":"e_1_3_2_1_2_1","unstructured":"Susan Collins, Yizhou Sun, Michal Kosinski, David Stillwell, and Natasha Marku-zon. 2015. Are You Satisfied with Life?: Predicting Satisfaction with Life from Facebook. In Social Computing, Behavioral-Cultural Modeling, and Prediction, Nitin Agarwal, Kevin Xu, and Nathaniel Osgood (Eds.). Springer International Publishing, Cham, 24--33."},{"key":"e_1_3_2_1_3_1","unstructured":"Yinpeng Dong Hang Su Jun Zhu and Fan Bao. 2017. Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples. arXiv:1708.05493 [cs.CV]"},{"key":"e_1_3_2_1_4_1","volume-title":"Steepest descent methods for multicriteria optimization. Math Methods Oper Res 51 (08","author":"Fliege J\u00f6rg","year":"2000","unstructured":"J\u00f6rg Fliege and Benar Svaiter. 2000. Steepest descent methods for multicriteria optimization. Math Methods Oper Res 51 (08 2000), 479--494. https:\/\/doi.org\/10. 1007\/s001860000043"},{"key":"e_1_3_2_1_5_1","volume-title":"3rd 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 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). ICLR, San Diego, CA, USA, https:\/\/arxiv.org\/abs\/1412.6572. http:\/\/arxiv.org\/abs\/1412.6572"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_7_1","volume-title":"Improving neural networks by preventing co-adaptation of feature detectors. CoRR abs\/1207.0580","author":"Hinton Geoffrey E.","year":"2012","unstructured":"Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. CoRR abs\/1207.0580 (2012), 2012. arXiv:1207.0580 http: \/\/arxiv.org\/abs\/1207.0580"},{"key":"e_1_3_2_1_8_1","volume-title":"fastai: A Layered API for Deep Learning. CoRR abs\/2002.04688","author":"Howard Jeremy","year":"2020","unstructured":"Jeremy Howard and Sylvain Gugger. 2020. fastai: A Layered API for Deep Learning. CoRR abs\/2002.04688 (2020), https:\/\/arxiv.org\/abs\/2002.04688. arXiv:2002.04688 https:\/\/arxiv.org\/abs\/2002.04688"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the 32nd International Conference on International Conference on Machine Learning -","volume":"37","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37 (ICML'15). JMLR.org, Lille, France, 448--456."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32523-7_13"},{"key":"e_1_3_2_1_11_1","volume-title":"Torchattacks: A Pytorch Repository for Adversarial Attacks. ArXiv abs\/2010.01950","author":"Kim Hoki","year":"2020","unstructured":"Hoki Kim. 2020. Torchattacks: A Pytorch Repository for Adversarial Attacks. ArXiv abs\/2010.01950 (2020)."},{"key":"e_1_3_2_1_12_1","volume-title":"Captum: A unified and generic model interpretability library for PyTorch. arXiv:2009.07896 [cs.LG]","author":"Kokhlikyan Narine","year":"2020","unstructured":"Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. 2020. Captum: A unified and generic model interpretability library for PyTorch. arXiv:2009.07896 [cs.LG]"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-79310-1"},{"key":"e_1_3_2_1_14_1","volume-title":"Adversarial Machine Learning at Scale. CoRR abs\/1611.01236","author":"Kurakin Alexey","year":"2016","unstructured":"Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2016. Adversarial Machine Learning at Scale. CoRR abs\/1611.01236 (2016), 2017. arXiv:1611.01236 http: \/\/arxiv.org\/abs\/1611.01236"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v10i1.14738"},{"key":"e_1_3_2_1_16_1","volume-title":"The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning. CoRR abs\/1907.04472","author":"Liu Suyun","year":"2019","unstructured":"Suyun Liu and Lu\u00eds Nunes Vicente. 2019. The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning. CoRR abs\/1907.04472 (2019), 2019. arXiv:1907.04472 http: \/\/arxiv.org\/abs\/1907.04472"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Scott","key":"e_1_3_2_1_18_1","unstructured":"Scott M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 4768--4777."},{"key":"e_1_3_2_1_19_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Madry Aleksander","year":"2018","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=rJzIBfZAb"},{"key":"e_1_3_2_1_20_1","unstructured":"Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras and Adrian Vladu. 2019. Towards Deep Learning Models Resistant to Adversarial Attacks. arXiv:1706.06083 [stat.ML]"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206656"},{"key":"e_1_3_2_1_22_1","volume-title":"Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers? CoRR abs\/2006.03833","author":"Melacci Stefano","year":"2020","unstructured":"Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu, Ambra Demontis, Battista Biggio, Marco Gori, and Fabio Roli. 2020. Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers? CoRR abs\/2006.03833 (2020), https:\/\/arxiv.org\/abs\/2006.03833. arXiv:2006.03833 https:\/\/arxiv.org\/abs\/2006. 03833"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"e_1_3_2_1_24_1","volume-title":"Perdomo and Yaron Singer","author":"Juan","year":"2019","unstructured":"Juan C. Perdomo and Yaron Singer. 2019. Robust Attacks against Multiple Classi- fiers. CoRR abs\/1906.02816 (2019). arXiv:1906.02816 http:\/\/arxiv.org\/abs\/1906. 02816"},{"key":"e_1_3_2_1_25_1","volume-title":"Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models. CoRR abs\/1707.04131","author":"Rauber Jonas","year":"2017","unstructured":"Jonas Rauber, Wieland Brendel, and Matthias Bethge. 2017. Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models. CoRR abs\/1707.04131 (2017), http:\/\/arxiv.org\/abs\/1707.04131. arXiv:1707.04131 http:\/\/arxiv.org\/abs\/1707.04131"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660460.2660470"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1177\/1948550611424968"},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning -","volume":"70","author":"Sundararajan Mukund","year":"2017","unstructured":"Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML'17). JMLR.org, Sydney, NSW, Australia, 3319--3328."},{"key":"e_1_3_2_1_30_1","volume-title":"2nd International Conference on Learning Representations, ICLR","author":"Szegedy Christian","year":"2014","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). ICLR, Banff, AB, Canada, http:\/\/arxiv.org\/abs\/1312.6199. http: \/\/arxiv.org\/abs\/1312.6199"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727230"},{"key":"e_1_3_2_1_32_1","unstructured":"Florian Tram\u00e8r Nicolas Papernot Ian Goodfellow Dan Boneh and Patrick McDaniel. 2017. The Space of Transferable Adversarial Examples. arXiv:1704.03453 [stat.ML]"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1037\/pspa0000098"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1187"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-019-1211-x"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.463"}],"event":{"name":"CCS '23: ACM SIGSAC Conference on Computer and Communications Security","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"],"location":"Copenhagen Denmark","acronym":"CCS '23"},"container-title":["Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576915.3623177","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3576915.3623177","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3576915.3623177","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T01:49:23Z","timestamp":1755740963000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576915.3623177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"references-count":37,"alternative-id":["10.1145\/3576915.3623177","10.1145\/3576915"],"URL":"https:\/\/doi.org\/10.1145\/3576915.3623177","relation":{},"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"2023-11-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}