{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T01:47:24Z","timestamp":1768873644214,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s13042-024-02097-4","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T19:02:52Z","timestamp":1708023772000},"page":"3367-3378","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dual stage black-box adversarial attack against vision transformer"],"prefix":"10.1007","volume":"15","author":[{"given":"Fan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Mingwen","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Lingzhuang","family":"Meng","sequence":"additional","affiliation":[]},{"given":"Fukang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"2097_CR1","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A et al (2022) Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst 35:27730\u201327744","journal-title":"Adv Neural Inf Process Syst"},{"key":"2097_CR2","unstructured":"Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J\u00e9gou H (2021) Training data-efficient image transformers & distillation through attention. In: International conference on machine learning, pp 10347\u201310357"},{"key":"2097_CR3","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint. arXiv:2010.11929"},{"key":"2097_CR4","doi-asserted-by":"publisher","unstructured":"Yuan L, Chen Y, Wang T, Yu W, Shi Y, Jiang Z-H, Tay FEH, Feng J, Yan S (2021) Tokens-to-token ViT: training vision transformers from scratch on ImageNet. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 558\u2013567. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00060","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"2097_CR5","first-page":"15908","volume":"34","author":"K Han","year":"2021","unstructured":"Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y (2021) Transformer in transformer. Adv Neural Inf Process Syst 34:15908\u201315919","journal-title":"Adv Neural Inf Process Syst"},{"key":"2097_CR6","doi-asserted-by":"crossref","unstructured":"Heo B, Yun S, Han D, Chun S, Choe J, Oh SJ (2021) Rethinking spatial dimensions of vision transformers. In: Proceedings of the IEEE\/CVF International conference on computer vision (ICCV) pp. 11936\u201311945","DOI":"10.1109\/ICCV48922.2021.01172"},{"key":"2097_CR7","doi-asserted-by":"publisher","unstructured":"Touvron H, Cord M, Sablayrolles A, Synnaeve G, J\u00e9gou H (2021) Going deeper with image transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 32\u201342. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00010","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"2097_CR8","doi-asserted-by":"publisher","unstructured":"Graham B, El-Nouby A, Touvron H, Stock P, Joulin A, J\u00e9gou H, Douze M (2021) Levit: a vision transformer in convnet\u2019s clothing for faster inference. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 12259\u201312269. https:\/\/doi.org\/10.1109\/ICCV48922.2021.01204","DOI":"10.1109\/ICCV48922.2021.01204"},{"key":"2097_CR9","doi-asserted-by":"publisher","unstructured":"d\u2019Ascoli S, Touvron H, Leavitt ML, Morcos AS, Biroli G, Sagun L (2021) Convit: improving vision transformers with soft convolutional inductive biases. In: International conference on machine learning, pp 2286\u20132296. https:\/\/doi.org\/10.1088\/1742-5468\/ac9830","DOI":"10.1088\/1742-5468\/ac9830"},{"key":"2097_CR10","doi-asserted-by":"publisher","unstructured":"Chen Z, Xie L, Niu J, Liu X, Wei L, Tian Q (2021) Visformer: the vision-friendly transformer. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 589\u2013598. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00063","DOI":"10.1109\/ICCV48922.2021.00063"},{"key":"2097_CR11","doi-asserted-by":"publisher","first-page":"16929","DOI":"10.1007\/s11042-023-16300-1","volume":"83","author":"FS Gharehchopogh","year":"2024","unstructured":"Gharehchopogh FS, Ibrikci T (2024) An improved African vultures optimization algorithm using different fitness functionsfor multi-level thresholding image segmentation. Multimed Tools Appl 83:16929\u201316975.  https:\/\/doi.org\/10.1007\/s11042-023-16300-1","journal-title":"Multimed Tools Appl"},{"key":"2097_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s42235-023-00441-y","author":"E \u00d6zbay","year":"2023","unstructured":"\u00d6zbay E, \u00d6zbay FA, Gharehchopogh FS ((2023) Peripheral blood smear images classification for acute lymphoblastic leukemia diagnosis with an improved convolutional neural network. J Bionic Eng. https:\/\/doi.org\/10.1007\/s42235-023-00441-y","journal-title":"J Bionic Eng"},{"issue":"4","key":"2097_CR13","doi-asserted-by":"publisher","first-page":"2683","DOI":"10.1007\/s11831-023-09883-3","volume":"30","author":"FS Gharehchopogh","year":"2023","unstructured":"Gharehchopogh FS, Ucan A, Ibrikci T, Arasteh B, Isik G (2023) Slime mould algorithm: a comprehensive survey of its variants and applications. Arch Comput Methods Eng 30(4):2683\u20132723","journal-title":"Arch Comput Methods Eng"},{"key":"2097_CR14","doi-asserted-by":"crossref","unstructured":"Xie C, Zhang Z, Zhou Y, Bai S, Wang J, Ren Z, Yuille AL (2019) Improving transferability of adversarial examples with input diversity. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2730\u20132739","DOI":"10.1109\/CVPR.2019.00284"},{"key":"2097_CR15","doi-asserted-by":"publisher","unstructured":"Dong Y, Pang T, Su H, Zhu J (2019) Evading defenses to transferable adversarial examples by translation-invariant attacks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4312\u20134321. https:\/\/doi.org\/10.1109\/CVPR.2019.00444","DOI":"10.1109\/CVPR.2019.00444"},{"key":"2097_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3033291","author":"S Chen","year":"2020","unstructured":"Chen S, He Z, Sun C, Yang J, Huang X (2020) Universal adversarial attack on attention and the resulting dataset damagenet. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2020.3033291","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2097_CR17","first-page":"85","volume":"33","author":"Y Guo","year":"2020","unstructured":"Guo Y, Li Q, Chen H (2020) Backpropagating linearly improves transferability of adversarial examples. Adv Neural Inf Process Syst 33:85\u201395","journal-title":"Adv Neural Inf Process Syst"},{"key":"2097_CR18","unstructured":"Shao R, Shi Z, Yi J, Chen PY, Hsieh CJ (2021) On the adversarial robustness of visual transformers. arXiv preprint arXiv:2103.15670"},{"key":"2097_CR19","doi-asserted-by":"publisher","unstructured":"Bhojanapalli S, Chakrabarti A, Glasner D, Li D, Unterthiner T, Veit A (2021) Understanding robustness of transformers for image classification. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10231\u201310241. https:\/\/doi.org\/10.1109\/ICCV48922.2021.01007","DOI":"10.1109\/ICCV48922.2021.01007"},{"key":"2097_CR20","unstructured":"Naseer M, Ranasinghe K, Khan S, Khan FS, Porikli F (2021) On improving adversarial transferability of vision transformers. arXiv preprint arXiv:2106.04169"},{"key":"2097_CR21","doi-asserted-by":"publisher","unstructured":"Wei Z, Chen J, Goldblum M, Wu Z, Goldstein T, Jiang Y-G (2022) Towards transferable adversarial attacks on vision transformers. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 2668\u20132676. https:\/\/doi.org\/10.1609\/aaai.v36i3.20169","DOI":"10.1609\/aaai.v36i3.20169"},{"key":"2097_CR22","doi-asserted-by":"crossref","unstructured":"Luo C, Lin Q, Xie W, Wu B, Xie J, Shen L (2022) Frequency-driven imperceptible adversarial attack on semantic similarity. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 15315\u201315324","DOI":"10.1109\/CVPR52688.2022.01488"},{"key":"2097_CR23","unstructured":"Yuan S, Zhang Q, Gao L, Cheng Y, Song J (2022) Natural color fool: towards boosting black-box unrestricted attacks. arXiv preprint. arXiv:2210.02041"},{"key":"2097_CR24","doi-asserted-by":"publisher","unstructured":"Papernot N, McDaniel P, Goodfellow I, Jha S, Celik ZB, Swami A (2017) Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia conference on computer and communications security, pp 506\u2013519. https:\/\/doi.org\/10.1145\/3052973.3053009","DOI":"10.1145\/3052973.3053009"},{"key":"2097_CR25","unstructured":"Brendel W, Rauber J, Bethge M (2017) Decision-based adversarial attacks: reliable attacks against black-box machine learning models. arXiv preprint arXiv:1712.04248"},{"key":"2097_CR26","unstructured":"Shi Y, Han Y (2021) Decision-based black-box attack against vision transformers via patch-wise adversarial removal. arXiv preprint arXiv:2112.03492"},{"key":"2097_CR27","unstructured":"Zhang Q, Li X, Chen Y, Song J, Gao L, He Y, Xue H (2022) Beyond imagenet attack: towards crafting adversarial examples for black-box domains. arXiv preprint arXiv:2201.11528"},{"key":"2097_CR28","first-page":"1","volume-title":"In: European conference on computer vision","author":"Z Yuan","year":"2022","unstructured":"Yuan Z, Zhang J, Shan S (2022) Adaptive image transformations for transfer-based adversarial attack. In: European conference on computer vision. Springer Nature Switzerland, Cham, pp 1\u201317"},{"key":"2097_CR29","doi-asserted-by":"publisher","unstructured":"Wang X, He K (2021) Enhancing the transferability of adversarial attacks through variance tuning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1924\u20131933. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00196","DOI":"10.1109\/CVPR46437.2021.00196"},{"issue":"3","key":"2097_CR30","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1007\/s42235-022-00303-z","volume":"20","author":"FS Gharehchopogh","year":"2023","unstructured":"Gharehchopogh FS (2023) An improved Harris Hawks optimization algorithm with multi-strategy for community detection in social network. J Bionic Eng 20(3):1175\u20131197","journal-title":"J Bionic Eng"},{"key":"2097_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100952","volume":"24","author":"FS Gharehchopogh","year":"2023","unstructured":"Gharehchopogh FS, Abdollahzadeh B, Barshandeh S, Arasteh B (2023) A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT. Internet Things 24:100952","journal-title":"Internet Things"},{"key":"2097_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119269","volume":"215","author":"Y Shen","year":"2023","unstructured":"Shen Y, Zhang C, Gharehchopogh FS, Mirjalili S (2023) An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems. Expert Syst Appl 215:119269","journal-title":"Expert Syst Appl"},{"issue":"4","key":"2097_CR33","doi-asserted-by":"publisher","first-page":"894","DOI":"10.3390\/sym15040894","volume":"15","author":"FS Gharehchopogh","year":"2023","unstructured":"Gharehchopogh FS, Khargoush AA (2023) A chaotic-based interactive autodidactic school algorithm for data clustering problems and its application on COVID-19 disease detection. Symmetry 15(4):894","journal-title":"Symmetry"},{"issue":"15","key":"2097_CR34","doi-asserted-by":"publisher","first-page":"2742","DOI":"10.3390\/math10152742","volume":"10","author":"J Piri","year":"2022","unstructured":"Piri J, Mohapatra P, Acharya B, Gharehchopogh FS, Gerogiannis VC, Kanavos A, Manika S (2022) Feature selection using artificial gorilla troop optimization for biomedical data: a case analysis with COVID-19 data. Mathematics 10(15):2742","journal-title":"Mathematics"},{"key":"2097_CR35","doi-asserted-by":"crossref","unstructured":"Wang Y, Li J, Liu H, Wang Y, Wu Y, Huang F, Ji R (2022) Black-box dissector: towards erasing-based hard-label model stealing attack. In: European conference on computer vision, pp 192\u2013208","DOI":"10.1007\/978-3-031-20065-6_12"},{"key":"2097_CR36","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572"},{"key":"2097_CR37","unstructured":"Kurakin A, Goodfellow I, Bengio S (2016) Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236"},{"key":"2097_CR38","doi-asserted-by":"publisher","unstructured":"Dong Y, Liao F, Pang T, Su H, Zhu J, Hu X, Li J (2018) Boosting adversarial attacks with momentum. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9185\u20139193. https:\/\/doi.org\/10.1109\/CVPR.2018.00957","DOI":"10.1109\/CVPR.2018.00957"},{"key":"2097_CR39","unstructured":"Lin J, Song C, He K, Wang L, Hopcroft JE (2019) Nesterov accelerated gradient and scale invariance for adversarial attacks. arXiv preprint arXiv:1908.06281"},{"key":"2097_CR40","unstructured":"Wang X, Ren J, Lin S, Zhu X, Wang Y, Zhang Q (2020) A unified approach to interpreting and boosting adversarial transferability. arXiv preprint arXiv:2010.04055"},{"key":"2097_CR41","unstructured":"Wu D, Wang Y, Xia S-T, Bailey J, Ma X (2020) Skip connections matter: on the transferability of adversarial examples generated with ResNets. arXiv preprint arXiv:2002.05990"},{"key":"2097_CR42","doi-asserted-by":"crossref","unstructured":"Zhou W, Hou X, Chen Y, Tang M, Huang X, Gan X, Yang Y (2018) Transferable adversarial perturbations. In: Proceedings of the European conference on computer vision (ECCV), pp 452\u2013467","DOI":"10.1007\/978-3-030-01264-9_28"},{"key":"2097_CR43","doi-asserted-by":"crossref","unstructured":"Wu W, Su Y, Chen X, Zhao S, King I, Lyu MR, Tai Y-W (2020) Boosting the transferability of adversarial samples via attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1161\u20131170","DOI":"10.1109\/CVPR42600.2020.00124"},{"issue":"6","key":"2097_CR44","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"2097_CR45","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"2097_CR46","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"2097_CR47","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2097_CR48","doi-asserted-by":"publisher","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv preprint. arXiv:1605.07146. https:\/\/doi.org\/10.5244\/C.30.87","DOI":"10.5244\/C.30.87"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02097-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02097-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02097-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T07:27:59Z","timestamp":1719991679000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02097-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,15]]},"references-count":48,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["2097"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02097-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,15]]},"assertion":[{"value":"30 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2024","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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors agree to participate and approve the final manuscript and the submission to this journal.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}