{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:56:31Z","timestamp":1772823391176,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T00:00:00Z","timestamp":1702252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Major Science and Technology Project of Precious Metal Materials Genome Engineering in Yunnan Province","award":["202002AD080047"],"award-info":[{"award-number":["202002AD080047"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61762092"],"award-info":[{"award-number":["61762092"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province","award":["2020SE303"],"award-info":[{"award-number":["2020SE303"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09278-2","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T10:02:27Z","timestamp":1702288947000},"page":"4263-4280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A hybrid style transfer with whale optimization algorithm model for textual adversarial attack"],"prefix":"10.1007","volume":"36","author":[{"given":"Yan","family":"Kang","sequence":"first","affiliation":[]},{"given":"Jianjun","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xuekun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Baochen","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Wentao","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"key":"9278_CR1","doi-asserted-by":"crossref","unstructured":"Wang Y, Hu X (2022) Machine learning-based image recognition for rural architectural planning and design[J]. Neural Comput Appl, 1\u201310","DOI":"10.1007\/s00521-022-07799-w"},{"issue":"4","key":"9278_CR2","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1007\/s00521-021-06515-4","volume":"34","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Liu Y, Yang G, Song J (2022) Ssit: a sample selection-based incremental model training method for image recognition. Neural Comput Appl 34(4):3117\u20133134","journal-title":"Neural Comput Appl"},{"issue":"9","key":"9278_CR3","doi-asserted-by":"publisher","first-page":"6637","DOI":"10.1007\/s00521-021-06061-z","volume":"34","author":"P Qin","year":"2022","unstructured":"Qin P, Zhang C, Dang M (2022) Gvnet: Gaussian model with voxel-based 3d detection network for autonomous driving. Neural Comput Appl 34(9):6637\u20136645","journal-title":"Neural Comput Appl"},{"issue":"18","key":"9278_CR4","doi-asserted-by":"publisher","first-page":"15981","DOI":"10.1007\/s00521-022-07278-2","volume":"34","author":"MS Rais","year":"2022","unstructured":"Rais MS, Zouaidia K, Boudour R (2022) Enhanced decision making in multi-scenarios for autonomous vehicles using alternative bidirectional Q network[J]. Neural Comput Appl 34(18):15981\u201315996","journal-title":"Neural Comput Appl"},{"key":"9278_CR5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6199","author":"C Szegedy","year":"2013","unstructured":"Szegedy C, Zaremba W, Sutskever I et al. (2013) Intriguing properties of neural networks[J]. Comput Sci.\u00a0https:\/\/doi.org\/10.48550\/arXiv.1312.6199","journal-title":"Comput Sci"},{"key":"9278_CR6","first-page":"20","volume":"1050","author":"IJ Goodfellow","year":"2015","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples. Stat 1050:20","journal-title":"Stat"},{"issue":"3","key":"9278_CR7","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1007\/s00521-017-3100-y","volume":"31","author":"SO Olatunji","year":"2019","unstructured":"Olatunji SO (2019) Improved email spam detection model based on support vector machines. Neural Comput Appl 31(3):691\u2013699","journal-title":"Neural Comput Appl"},{"issue":"9","key":"9278_CR8","doi-asserted-by":"publisher","first-page":"4239","DOI":"10.1007\/s00521-019-04331-5","volume":"32","author":"A Barushka","year":"2020","unstructured":"Barushka A, Hajek P (2020) Spam detection on social networks using cost-sensitive feature selection and ensemble-based regularized deep neural networks. Neural Comput Appl 32(9):4239\u20134257","journal-title":"Neural Comput Appl"},{"issue":"24","key":"9278_CR9","doi-asserted-by":"publisher","first-page":"22275","DOI":"10.1007\/s00521-022-07698-0","volume":"34","author":"H Yan","year":"2022","unstructured":"Yan H, Yi B, Li H, Wu D (2022) Sentiment knowledge-induced neural network for aspect-level sentiment analysis. Neural Comput Appl 34(24):22275\u201322286","journal-title":"Neural Comput Appl"},{"issue":"22","key":"9278_CR10","doi-asserted-by":"publisher","first-page":"19441","DOI":"10.1007\/s00521-022-07509-6","volume":"34","author":"N Passalis","year":"2022","unstructured":"Passalis N, Avramelou L, Seficha S, Tsantekidis A, Doropoulos S, Makris G, Tefas A (2022) Multisource financial sentiment analysis for detecting bitcoin price change indications using deep learning. Neural Comput Appl 34(22):19441\u201319452","journal-title":"Neural Comput Appl"},{"issue":"23","key":"9278_CR11","doi-asserted-by":"publisher","first-page":"15955","DOI":"10.1007\/s00521-021-06444-2","volume":"33","author":"L Huang","year":"2021","unstructured":"Huang L, Chen W, Liu Y, Zhang H, Qu H (2021) Improving neural machine translation using gated state network and focal adaptive attention network. Neural Comput Appl 33(23):15955\u201315967","journal-title":"Neural Comput Appl"},{"issue":"17","key":"9278_CR12","doi-asserted-by":"publisher","first-page":"14823","DOI":"10.1007\/s00521-022-07337-8","volume":"34","author":"SM Singh","year":"2022","unstructured":"Singh SM, Singh TD (2022) An empirical study of low-resource neural machine translation of manipuri in multilingual settings[J]. Neural Comput Appl 34(17):14823\u201314844","journal-title":"Neural Comput Appl"},{"key":"9278_CR13","unstructured":"Hosseini H, Kannan S, Zhang B, Poovendran R (2017) Deceiving Google\u2019s perspective API built for detecting toxic comments. arXiv preprint arXiv:1702.08138"},{"key":"9278_CR14","doi-asserted-by":"crossref","unstructured":"Li L, Ma R, Guo Q, Xue X, Qiu X (2020) Bert-attack: Adversarial attack against Bert using Bert. In: Proceedings of the 2020 conference on empirical methods in natural language processing, pp 6193\u20136202","DOI":"10.18653\/v1\/2020.emnlp-main.500"},{"issue":"3","key":"9278_CR15","first-page":"1","volume":"11","author":"WE Zhang","year":"2020","unstructured":"Zhang WE, Sheng QZ, Alhazmi A, Li C (2020) Adversarial attacks on deep-learning models in natural language processing: a survey. ACM Trans Intell Syst Technol 11(3):1\u201341","journal-title":"ACM Trans Intell Syst Technol"},{"key":"9278_CR16","doi-asserted-by":"crossref","unstructured":"Wang W, Wang R, Wang L, et al. (2021) Towards a robust deep neural network against adversarial texts: A survey[J]. IEEE Trans Knowledge Data Eng","DOI":"10.1109\/TKDE.2021.3117608"},{"key":"9278_CR17","unstructured":"Belinkov Y, Bisk Y (2018) Synthetic and natural noise both break neural machine translation. In: International conference on learning representations"},{"key":"9278_CR18","doi-asserted-by":"crossref","unstructured":"Ebrahimi J, Rao A, Lowd D, Dou D (2018) Hotflip: white-box adversarial examples for text classification. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 31\u201336","DOI":"10.18653\/v1\/P18-2006"},{"key":"9278_CR19","doi-asserted-by":"crossref","unstructured":"Gil Y, Chai Y, Gorodissky O, Berant J (2019) 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, pp 1373\u20131379","DOI":"10.18653\/v1\/N19-1139"},{"key":"9278_CR20","doi-asserted-by":"crossref","unstructured":"Alzantot M, Sharma Y, Elgohary A, Ho B-J, Srivastava M, Chang K-W (2018) Generating natural language adversarial examples. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 2890\u20132896","DOI":"10.18653\/v1\/D18-1316"},{"key":"9278_CR21","doi-asserted-by":"crossref","unstructured":"Ren S, Deng Y, He K, Che W (2019) Generating natural language adversarial examples through probability weighted word saliency. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1085\u20131097","DOI":"10.18653\/v1\/P19-1103"},{"key":"9278_CR22","doi-asserted-by":"crossref","unstructured":"Jin D, Jin Z, Zhou JT, Szolovits P (2020) 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","DOI":"10.1609\/aaai.v34i05.6311"},{"key":"9278_CR23","doi-asserted-by":"crossref","unstructured":"Tsai Y-T, Yang M-C, Chen H-Y (2019) Adversarial attack on sentiment classification. In: Proceedings of the 2019 ACL workshop BlackboxNLP: analyzing and interpreting neural networks for NLP, pp 233\u2013240","DOI":"10.18653\/v1\/W19-4824"},{"key":"9278_CR24","doi-asserted-by":"crossref","unstructured":"Zang Y, Qi F, Yang C, Liu Z, Zhang M, Liu Q, Sun M (2020) Word-level textual adversarial attacking as combinatorial optimization. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 6066\u20136080","DOI":"10.18653\/v1\/2020.acl-main.540"},{"key":"9278_CR25","doi-asserted-by":"crossref","unstructured":"Yang X, Liu W, Tao D, Liu W (2021) Besa: Bert-based simulated annealing for adversarial text attacks. In: Proceedings of the 30th international joint conference on artificial intelligence, pp. 3293\u20133299","DOI":"10.24963\/ijcai.2021\/453"},{"key":"9278_CR26","doi-asserted-by":"crossref","unstructured":"Jia R, Liang P (2017) Adversarial examples for evaluating reading comprehension systems. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2021\u20132031","DOI":"10.18653\/v1\/D17-1215"},{"key":"9278_CR27","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2018) Semantically equivalent adversarial rules for debugging NLP models. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 856\u2013865","DOI":"10.18653\/v1\/P18-1079"},{"key":"9278_CR28","doi-asserted-by":"crossref","unstructured":"Wang T, Wang X, Qin Y, Packer B, Li K, Chen J, Beutel A, Chi E (2020) 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","DOI":"10.18653\/v1\/2020.emnlp-main.417"},{"key":"9278_CR29","doi-asserted-by":"crossref","unstructured":"Qi F, Chen Y, Zhang X, Li M, Liu Z, Sun M (2021) Mind the style of text! adversarial and backdoor attacks based on text style transfer. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 4569\u20134580","DOI":"10.18653\/v1\/2021.emnlp-main.374"},{"key":"9278_CR30","unstructured":"Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2018) Towards deep learning models resistant to adversarial attacks. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=rJzIBfZAb"},{"key":"9278_CR31","unstructured":"Wu T, Tong L, Vorobeychik Y (2020) Defending against physically realizable attacks on image classification. In: International conference on learning representations. https:\/\/openreview.net\/forum?id=H1xscnEKDr"},{"key":"9278_CR32","unstructured":"Zhou D, Liu T, Han B, Wang N, Peng C, Gao X (2021) Towards defending against adversarial examples via attack-invariant features. In: International conference on machine learning. PMLR, pp 12835\u201312845"},{"key":"9278_CR33","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"key":"9278_CR34","doi-asserted-by":"crossref","unstructured":"John V, Mou L, Bahuleyan H, Vechtomova O (2019) Disentangled representation learning for non-parallel text style transfer. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 424\u2013434","DOI":"10.18653\/v1\/P19-1041"},{"key":"9278_CR35","unstructured":"Wang K, Hua H, Wan X (2019) Controllable unsupervised text attribute transfer via editing entangled latent representation[J]. Adv Neural Info Process Syst 32"},{"key":"9278_CR36","doi-asserted-by":"crossref","unstructured":"Dai N, Liang J, Qiu X, Huang X-J (2019) Style transformer: unpaired text style transfer without disentangled latent representation. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5997\u20136007","DOI":"10.18653\/v1\/P19-1601"},{"key":"9278_CR37","unstructured":"He J, Wang X, Neubig G, Berg-Kirkpatrick T (2019) A probabilistic formulation of unsupervised text style transfer. In: International conference on learning representations"},{"issue":"3","key":"9278_CR38","doi-asserted-by":"publisher","first-page":"153","DOI":"10.2307\/408741","volume":"2","author":"L Bloomfield","year":"1926","unstructured":"Bloomfield L (1926) A set of postulates for the science of language. Language 2(3):153\u2013164","journal-title":"Language"},{"key":"9278_CR39","doi-asserted-by":"crossref","unstructured":"Dong Z, Dong Q (2006) Hownet and the computation of meaning. World Scientific Publishing Co., Inc","DOI":"10.1142\/9789812774675"},{"issue":"6","key":"9278_CR40","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1063\/1.1699114","volume":"21","author":"N Metropolis","year":"1953","unstructured":"Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087\u20131092","journal-title":"J Chem Phys"},{"issue":"4598","key":"9278_CR41","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1126\/science.220.4598.671","volume":"220","author":"S Kirkpatrick","year":"1983","unstructured":"Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671\u2013680","journal-title":"Science"},{"key":"9278_CR42","doi-asserted-by":"crossref","unstructured":"Krishna K, Wieting J, Iyyer M (2020) Reformulating unsupervised style transfer as paraphrase generation. In: Proceedings of the 2020 conference on empirical methods in natural language processing","DOI":"10.18653\/v1\/2020.emnlp-main.55"},{"issue":"8","key":"9278_CR43","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I et al (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8):9","journal-title":"OpenAI Blog"},{"key":"9278_CR44","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2019) Sentence-bert: sentence embeddings using siamese bert-networks. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 3982\u20133992","DOI":"10.18653\/v1\/D19-1410"},{"key":"9278_CR45","unstructured":"Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631\u20131642"},{"key":"9278_CR46","unstructured":"Maas A, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 142\u2013150"},{"key":"9278_CR47","doi-asserted-by":"crossref","unstructured":"Bowman SR, Angeli G, Potts C, Manning CD (2015) A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 conference on empirical methods in natural language processing","DOI":"10.18653\/v1\/D15-1075"},{"key":"9278_CR48","doi-asserted-by":"crossref","unstructured":"de Gibert O, P\u00e9rez N, Garc\u00eda-Pablos A, Cuadros M (2018) Hate speech dataset from a white supremacy forum. In: Proceedings of the 2nd workshop on abusive language online, pp 11\u201320","DOI":"10.18653\/v1\/W18-5102"},{"key":"9278_CR49","unstructured":"Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification[J]. Adv Neural Info Process Syst 28"},{"key":"9278_CR50","doi-asserted-by":"crossref","unstructured":"Conneau A, Kiela D, Schwenk H, Barrault L, Bordes A (2017) Supervised learning of universal sentence representations from natural language inference data. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 670\u2013680","DOI":"10.18653\/v1\/D17-1070"},{"key":"9278_CR51","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) 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, pp 4171\u20134186"},{"key":"9278_CR52","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) Albert: a lite bert for self-supervised learning of language representations. In: International conference on learning representations"},{"key":"9278_CR53","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T (2019) Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108"},{"key":"9278_CR54","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"9278_CR55","doi-asserted-by":"crossref","unstructured":"Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, et al. (2020) Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, pp 38\u201345","DOI":"10.18653\/v1\/2020.emnlp-demos.6"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09278-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09278-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09278-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T10:13:48Z","timestamp":1707732828000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09278-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,11]]},"references-count":55,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9278"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09278-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,11]]},"assertion":[{"value":"31 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}