{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:03:11Z","timestamp":1743076991185,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200984"},{"type":"electronic","value":"9783031200991"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-20099-1_9","type":"book-chapter","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:04:11Z","timestamp":1673535851000},"page":"104-117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Textual Adversarial Attack Scheme for\u00a0Domain-Specific Models"],"prefix":"10.1007","author":[{"given":"Jialiang","family":"Dong","sequence":"first","affiliation":[]},{"given":"Shen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Longfei","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Huoyuan","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Zhitao","family":"Guan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"9_CR1","unstructured":"Ebrahimi, J., Lowd, D., Dou, D.: On adversarial examples for character-level neural machine translation. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 653\u2013663, Association for Computational Linguistics, Santa Fe, New Mexico, USA, August 2018"},{"issue":"43","key":"9_CR2","first-page":"1","volume":"21","author":"P Yang","year":"2020","unstructured":"Yang, P., Chen, J., Hsieh, C.J., Wang, J.L., Jordan, M.I.: Greedy attack and gumbel attack: generating adversarial examples for discrete data. J. Mach. Learn. Res. 21(43), 1\u201336 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Ebrahimi, J., Rao, A., Lowd, D., Dou, D.: HotFlip: white-box adversarial examples for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguistics, pp. 31\u201336, Melbourne, Australia, July 2018","DOI":"10.18653\/v1\/P18-2006"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Gil, Y., Chai, Y., Gorodissky, O., Berant, J.: White-to-black: efficient distillation of black-box adversarial attacks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics, pp. 1373\u20131379, Minneapolis, Minnesota, June 2019","DOI":"10.18653\/v1\/N19-1139"},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/TASLP.2021.3130970","volume":"30","author":"S Liu","year":"2021","unstructured":"Liu, S., Ning, L., Chen, C., Tang, K.: Efficient combinatorial optimization for word-level adversarial textual attack. IEEE\/ACM Trans. Audio Speech Lang. Process. 30, 98\u2013111 (2021)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Wang, T., et al.: CAT-gen: improving robustness in NLP models via controlled adversarial text generation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, pp. 5141\u20135146, Online, November 2020","DOI":"10.18653\/v1\/2020.emnlp-main.417"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Ren, S., Deng, Y., He, K., Che, W.: Generating natural language adversarial examples through probability weighted word saliency. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 1085\u20131097, Florence, Italy, July 2019","DOI":"10.18653\/v1\/P19-1103"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Li, L., Ma, R., Guo, Q., Xue, X., Qiu, X.: BERT-ATTACK: adversarial attack against BERT using BERT. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6193\u20136202. Association for Computational Linguistics, November 2020","DOI":"10.18653\/v1\/2020.emnlp-main.500"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Pruthi, D., Dhingra, B., Lipton, Z.C.: Combating adversarial misspellings with robust word recognition. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5582\u20135591. Association for Computational Linguistics, Florence, Italy, July 2019","DOI":"10.18653\/v1\/P19-1561"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Alzantot, M., Sharma, Y., Elgohary, A., Ho, B.J., Srivastava, M., Chang, K.W.: Generating natural language adversarial examples. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2890\u20132896. Association for Computational Linguistics, Brussels, Belgium, October-November 2018","DOI":"10.18653\/v1\/D18-1316"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Zang, Y., et al.: Word-level textual adversarial attacking as combinatorial optimization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6066\u20136080. Association for Computational Linguistics, July 2020","DOI":"10.18653\/v1\/2020.acl-main.540"},{"key":"9_CR12","unstructured":"Dong, Z., Dong, Q., Hao, C.: HowNet and its computation of meaning. In: Coling 2010: Demonstrations, pp. 53\u201356. Coling 2010 Organizing Committee, Beijing, China, August 2010"},{"key":"9_CR13","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, CD.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Jin, D., Jin, Z., Zhou, J.T., Szolovits, P.: Is bert really robust? a strong baseline for natural language attack on text classification and entailment. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8018\u20138025 (2020)","DOI":"10.1609\/aaai.v34i05.6311"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Garg, S., Ramakrishnan, G.: BAE: BERT-based adversarial examples for text classification. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6174\u20136181. Association for Computational Linguistics, November 2020","DOI":"10.18653\/v1\/2020.emnlp-main.498"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Wang, B., Xu, C., Liu, X., Cheng, Y., Li, B.: Semattack: natural textual attacks via different semantic spaces. arXiv preprint arXiv:2205.01287 (2022)","DOI":"10.18653\/v1\/2022.findings-naacl.14"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Guo, C., Sablayrolles, A., J\u00e9gou, H., Kiela, D.:. Gradient-based adversarial attacks against text transformers. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 5747\u20135757. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, November 2021","DOI":"10.18653\/v1\/2021.emnlp-main.464"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Cheng, M., Yi, J., Chen, P.-Y., Zhang, H., Hsieh, C.-J.: Seq2sick: evaluating the robustness of sequence-to-sequence models with adversarial examples. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3601\u20133608 (2020)","DOI":"10.1609\/aaai.v34i04.5767"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Schick, T., Sch\u00fctze, H.: Attentive mimicking: better word embeddings by attending to informative contexts. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 489\u2013494. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019","DOI":"10.18653\/v1\/N19-1048"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Mrk\u0161i\u0107, N., et al.: Counter-fitting word vectors to linguistic constraints. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 142\u2013148. Association for Computational Linguistics, San Diego, California, June 2016","DOI":"10.18653\/v1\/N16-1018"},{"key":"9_CR22","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. Adv. Neural Inf. Process. Syst. 28 (2015)"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746\u20131751. Association for Computational Linguistics, Doha, Qatar, October 2014","DOI":"10.3115\/v1\/D14-1181"},{"key":"9_CR24","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019"},{"key":"9_CR25","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20099-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:18:47Z","timestamp":1673536727000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20099-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031200984","9783031200991"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20099-1_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}