{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T23:10:08Z","timestamp":1751411408463,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819666010","type":"print"},{"value":"9789819665990","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-6599-0_31","type":"book-chapter","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T22:32:30Z","timestamp":1751409150000},"page":"454-469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["$$\\textbf{OneWORD}$$: Adversarial Text Detection and\u00a0Prediction Restoration Using One-Word Perturbation"],"prefix":"10.1007","author":[{"given":"Hoang-Quoc","family":"Nguyen-Son","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seira","family":"Hidano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuhide","family":"Fukushima","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shinsaku","family":"Kiyomoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isao","family":"Echizen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Bao, R., Zheng, R., Ding, L., Zhang, Q., Tao, D.: CASN: class-aware score network for textual adversarial detection. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp. 671\u2013687 (2023)","DOI":"10.18653\/v1\/2023.acl-long.40"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Biju, E., Sriram, A., Kumar, P., Khapra, M.M.: Input-specific attention subnetworks for adversarial detection. In: Findings of the Association for Computational Linguistics (ACL), pp. 31\u201344 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.4"},{"key":"31_CR3","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Iyyer, M., Wieting, J., Gimpel, K., Zettlemoyer, L.: Adversarial example generation with syntactically controlled paraphrase networks. In: Proceedings of the 16th Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pp. 1875\u20131885 (2018)","DOI":"10.18653\/v1\/N18-1170"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Jin, D., Jin, Z., Tianyi\u00a0Zhou, J., Szolovits, P.: Is BERT really robust? A strong baseline for natural language attack on text classification and entailment. In: Proceedings of the 34th Conference on Artificial Intelligence (AAAI), pp. 8018\u20138025 (2020)","DOI":"10.1609\/aaai.v34i05.6311"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Le, T., Park, N., Lee, D.: SHIELD: defending textual neural networks against multiple black-box adversarial attacks with stochastic multi-expert patcher. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 6661\u20136674 (2022)","DOI":"10.18653\/v1\/2022.acl-long.459"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Mosca, E., Agarwal, S., Rando-Ramirez, J., Groh, G.: \u201cThat is a suspicious reaction!\u201d: interpreting logits variation to detect NLP adversarial attacks. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 7806\u20137816 (2022)","DOI":"10.18653\/v1\/2022.acl-long.538"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Mozes, M., Stenetorp, P., Kleinberg, B., Griffin, L.: Frequency-guided word substitutions for detecting textual adversarial examples. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 171\u2013186 (2021)","DOI":"10.18653\/v1\/2021.eacl-main.13"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Nguyen-Son, H.Q., Ung, H.Q., Hidano, S., Fukushima, K., Kiyomoto, S.: CHECKHARD: checking hard labels for adversarial text detection, prediction correction, and perturbed word suggestion. In: Findings of the Association for Computational Linguistics (EMNLP), pp. 2903\u20132913 (2022)","DOI":"10.18653\/v1\/2022.findings-emnlp.210"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Raina, V., Gales, M.: Residue-based natural language adversarial attack detection. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pp. 3836\u20133848 (2022)","DOI":"10.18653\/v1\/2022.naacl-main.281"},{"key":"31_CR11","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 (ACL), pp. 1085\u20131097 (2019)","DOI":"10.18653\/v1\/P19-1103"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Salinas, A., Morstatter, F.: The butterfly effect of altering prompts: How small changes and jailbreaks affect large language model performance. ArXiv preprint arXiv:2401.03729 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.275"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Wang, J., Bao, R., Zhang, Z., Zhao, H.: Distinguishing non-natural from natural adversarial samples for more robust pre-trained language model. In: Findings of the Association for Computational Linguistics (ACL), pp. 905\u2013915 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.73"},{"key":"31_CR14","unstructured":"Wang, X., Xiong, Y., He, K.: Randomized substitution and vote for textual adversarial example detection. In: Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI) (2022)"},{"key":"31_CR15","doi-asserted-by":"crossref","unstructured":"Yin, F., Li, Y., Hsieh, C.J., Chang, K.W.: ADDMU: detection of far-boundary adversarial examples with data and model uncertainty estimation. In: EMNLP (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.440"},{"key":"31_CR16","unstructured":"Zeng, J., Zheng, X., Xu, J., Li, L., Yuan, L., Huang, X.: Certified robustness to text adversarial attacks by randomized [mask]. arXiv preprint arXiv:2105.03743 (2021)"},{"key":"31_CR17","doi-asserted-by":"crossref","unstructured":"Zheng, R., et al.: Detecting adversarial samples through sharpness of loss landscape. In: Findings of the Association for Computational Linguistics (ACL), pp. 11282\u201311298 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.717"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6599-0_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T22:32:34Z","timestamp":1751409154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6599-0_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819666010","9789819665990"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6599-0_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}