{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T06:44:47Z","timestamp":1767422687702,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62222601, 62176033, 62221005 and 61936001"],"award-info":[{"award-number":["62222601, 62176033, 62221005 and 61936001"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Chonqing","award":["cstc2022ycjh-bgzxm0128, cstc2021ycjh-bgzxm0013, CSTB2023NSCQ-JQX0034 and CSTB2022NSCQ-MSX1588"],"award-info":[{"award-number":["cstc2022ycjh-bgzxm0128, cstc2021ycjh-bgzxm0013, CSTB2023NSCQ-JQX0034 and CSTB2022NSCQ-MSX1588"]}]},{"name":"key cooperation project of Chongqing municipal education commission","award":["HZ2021008"],"award-info":[{"award-number":["HZ2021008"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,30]]},"DOI":"10.1145\/3652583.3658083","type":"proceedings-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T06:30:40Z","timestamp":1717741840000},"page":"348-356","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Text Adversarial Defense via Granular-Ball Sample Enhancement"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5053-5201","authenticated-orcid":false,"given":"Zeli","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9084-8708","authenticated-orcid":false,"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5993-9563","authenticated-orcid":false,"given":"Shuyin","family":"Xia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2194-8146","authenticated-orcid":false,"given":"Longlong","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8521-5232","authenticated-orcid":false,"given":"Guoyin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"volume-title":"Generating Natural Language Adversarial Examples","author":"Alzantot Moustafa","key":"e_1_3_2_1_1_1","unstructured":"Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani B. Srivastava, and Kai-Wei Chang. 2018. Generating Natural Language Adversarial Examples. In EMNLP. Association for Computational Linguistics, 2890--2896."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00254"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Hanjie Chen and Yangfeng Ji. 2022. Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation. In AAAI. 10463--10472.","DOI":"10.1609\/aaai.v36i10.21289"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3049490"},{"key":"e_1_3_2_1_5_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1)","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1). Association for Computational Linguistics, 4171--4186."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Javid Ebrahimi Anyi Rao Daniel Lowd and Dejing Dou. 2018. HotFlip: White-Box Adversarial Examples for Text Classification. In ACL (2). 31--36.","DOI":"10.18653\/v1\/P18-2006"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2018.00016"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3169922"},{"key":"e_1_3_2_1_9_1","volume-title":"A Survey in Adversarial Defences and Robustness in NLP. arXiv e-prints","author":"Goyal Shreya","year":"2022","unstructured":"Shreya Goyal, Sumanth Doddapaneni, Mitesh M. Khapra, and Balaraman Ravindran. 2022. A Survey in Adversarial Defences and Robustness in NLP. arXiv e-prints (2022)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09838-1"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Pei Huang Yuting Yang Fuqi Jia Minghao Liu Feifei Ma and Jian Zhang. 2022. Word Level Robustness Enhancement: Fight Perturbation with Perturbation. In AAAI. 10785--10793.","DOI":"10.1609\/aaai.v36i10.21324"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Mohit Iyyer John Wieting Kevin Gimpel and Luke Zettlemoyer. 2018. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks. In NAACL-HLT. 1875--1885.","DOI":"10.18653\/v1\/N18-1170"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Robin Jia Aditi Raghunathan Kerem G\u00f6ksel and Percy Liang. 2019. Certified Robustness to Adversarial Word Substitutions. In EMNLP\/IJCNLP (1). 4127--4140.","DOI":"10.18653\/v1\/D19-1423"},{"key":"e_1_3_2_1_14_1","volume-title":"Joey Tianyi Zhou, and Peter Szolovits","author":"Jin Di","year":"2020","unstructured":"Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020. Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment. In AAAI. 8018--8025."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_3_2_1_16_1","unstructured":"Andrew L. Maas Raymond E. Daly Peter T. Pham Dan Huang Andrew Y. Ng and Christopher Potts. 2011. Learning Word Vectors for Sentiment Analysis. In ACL. 142--150."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi and Pascal Frossard. 2016. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks. In CVPR. 2574--2582.","DOI":"10.1109\/CVPR.2016.282"},{"key":"e_1_3_2_1_19_1","volume-title":"Griffin","author":"Mozes Maximilian","year":"2021","unstructured":"Maximilian Mozes, Pontus Stenetorp, Bennett Kleinberg, and Lewis D. Griffin. 2021. Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples. In EACL. 171--186."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2979670"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"e_1_3_2_1_22_1","volume-title":"Manning","author":"Pennington Jeffrey","year":"2014","unstructured":"Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global Vectors for Word Representation. In EMNLP. 1532--1543."},{"key":"e_1_3_2_1_23_1","unstructured":"Shuhuai Ren Yihe Deng Kun He and Wanxiang Che. 2019. Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency. In ACL (1). 1085--1097."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Boxin Wang Hengzhi Pei Boyuan Pan Qian Chen Shuohang Wang and Bo Li. 2020. T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack. In EMNLP (1). 6134--6150.","DOI":"10.18653\/v1\/2020.emnlp-main.495"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Fei Wang Mengqing Jiang Chen Qian Shuo Yang Cheng Li Honggang Zhang Xiaogang Wang and Xiaoou Tang. 2017. Residual Attention Network for Image Classification. In CVPR. 6450--6458.","DOI":"10.1109\/CVPR.2017.683"},{"key":"e_1_3_2_1_26_1","volume-title":"UAI (Proceedings of Machine Learning Research","volume":"833","author":"Wang Xiaosen","year":"2021","unstructured":"Xiaosen Wang, Jin Hao, Yichen Yang, and Kun He. 2021a. Natural language adversarial defense through synonym encoding. In UAI (Proceedings of Machine Learning Research, Vol. 161). 823--833."},{"key":"e_1_3_2_1_27_1","volume-title":"Hopcroft","author":"Wang Xiaosen","year":"2019","unstructured":"Xiaosen Wang, Kun He, and John E. Hopcroft. 2019. AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets. CoRR, Vol. abs\/1904.07793 (2019)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Xiaosen Wang Yichen Yang Yihe Deng and Kun He. 2021b. Adversarial Training with Fast Gradient Projection Method against Synonym Substitution Based Text Attacks. In AAAI. 13997--14005.","DOI":"10.1609\/aaai.v35i16.17648"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.01.010"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3008694"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2997039"},{"key":"e_1_3_2_1_32_1","volume-title":"Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. In NDSS.","author":"Xu Weilin","year":"2018","unstructured":"Weilin Xu, David Evans, and Yanjun Qi. 2018. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks. In NDSS."},{"key":"e_1_3_2_1_33_1","volume-title":"UAI (Proceedings of Machine Learning Research","volume":"2224","author":"Yang Yichen","year":"2022","unstructured":"Yichen Yang, Xiaosen Wang, and Kun He. 2022. Robust textual embedding against word-level adversarial attacks. In UAI (Proceedings of Machine Learning Research, Vol. 180). 2214--2224."},{"key":"e_1_3_2_1_34_1","unstructured":"Wenpeng Yin Mo Yu Bing Xiang Bowen Zhou and Hinrich Sch\u00fctze. 2016. Simple Question Answering by Attentive Convolutional Neural Network. In COLING. ACL 1746--1756."},{"key":"e_1_3_2_1_35_1","volume-title":"Generating Fluent Adversarial Examples for Natural Languages. CoRR","author":"Zhang Huangzhao","year":"2020","unstructured":"Huangzhao Zhang, Hao Zhou, Ning Miao, and Lei Li. 2020. Generating Fluent Adversarial Examples for Natural Languages. CoRR, Vol. abs\/2007.06174 (2020)."},{"key":"e_1_3_2_1_36_1","volume-title":"Junbo Jake Zhao, and Yann LeCun","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015. Character-level Convolutional Networks for Text Classification. In NIPS. 649--657."},{"key":"e_1_3_2_1_37_1","volume-title":"PAWS: Paraphrase Adversaries from Word Scrambling. In NAACL-HLT (1). 1298--1308.","author":"Zhang Yuan","year":"2019","unstructured":"Yuan Zhang, Jason Baldridge, and Luheng He. 2019. PAWS: Paraphrase Adversaries from Word Scrambling. In NAACL-HLT (1). 1298--1308."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Yichao Zhou Jyun-Yu Jiang Kai-Wei Chang and Wei Wang. 2019. Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification. In EMNLP\/IJCNLP (1). 4903--4912.","DOI":"10.18653\/v1\/D19-1496"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Yi Zhou Xiaoqing Zheng Cho-Jui Hsieh Kai-Wei Chang and Xuanjing Huang. 2021. Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble. In ACL\/IJCNLP (1). 5482--5492.","DOI":"10.18653\/v1\/2021.acl-long.426"}],"event":{"name":"ICMR '24: International Conference on Multimedia Retrieval","sponsor":["SIGMM ACM Special Interest Group on Multimedia","SIGSOFT ACM Special Interest Group on Software Engineering"],"location":"Phuket Thailand","acronym":"ICMR '24"},"container-title":["Proceedings of the 2024 International Conference on Multimedia Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652583.3658083","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652583.3658083","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T08:54:21Z","timestamp":1755766461000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652583.3658083"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,30]]},"references-count":39,"alternative-id":["10.1145\/3652583.3658083","10.1145\/3652583"],"URL":"https:\/\/doi.org\/10.1145\/3652583.3658083","relation":{},"subject":[],"published":{"date-parts":[[2024,5,30]]},"assertion":[{"value":"2024-06-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}