{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:39:42Z","timestamp":1771306782080,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T00:00:00Z","timestamp":1731110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T00:00:00Z","timestamp":1731110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073263"],"award-info":[{"award-number":["62073263"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01628-4","type":"journal-article","created":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T06:39:29Z","timestamp":1731134369000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing adversarial transferability with local transformation"],"prefix":"10.1007","volume":"11","author":[{"given":"Yang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jinbang","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Haifeng","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8389-1093","authenticated-orcid":false,"given":"Peican","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Qun","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,9]]},"reference":[{"key":"1628_CR1","unstructured":"Tang K, Ma Y, Miao D, Song P, Gu Z, Tian Z, Wang W (2022) Decision fusion networks for image classification. IEEE Trans Neural Netw Learn Syst"},{"key":"1628_CR2","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1109\/TIP.2022.3148876","volume":"31","author":"S Chan","year":"2022","unstructured":"Chan S, Tao J, Zhou X, Bai C, Zhang X (2022) Siamese implicit region proposal network with compound attention for visual tracking. IEEE Trans Image Process 31:1882\u20131894","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"1628_CR3","doi-asserted-by":"publisher","first-page":"7451","DOI":"10.1007\/s40747-023-01135-y","volume":"9","author":"Z Peng","year":"2023","unstructured":"Peng Z, Song X, Song S, Stojanovic V (2023) Hysteresis quantified control for switched reaction\u2013diffusion systems and its application. Complex Intell Syst 9(6):7451\u20137460","journal-title":"Complex Intell Syst"},{"issue":"21","key":"1628_CR4","doi-asserted-by":"publisher","first-page":"15429","DOI":"10.1007\/s00521-023-08361-y","volume":"35","author":"X Song","year":"2023","unstructured":"Song X, Sun P, Song S, Stojanovic V (2023) Quantized neural adaptive finite-time preassigned performance control for interconnected nonlinear systems. Neural Comput Appl 35(21):15429\u201315446","journal-title":"Neural Comput Appl"},{"key":"1628_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cnsns.2024.107945","volume":"132","author":"X Song","year":"2024","unstructured":"Song X, Peng Z, Song S, Stojanovic V (2024) Anti-disturbance state estimation for pdt-switched rdnns utilizing time-sampling and space-splitting measurements. Commun Nonlinear Sci Numer Simul 132:107945","journal-title":"Commun Nonlinear Sci Numer Simul"},{"issue":"5","key":"1628_CR6","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1109\/JBHI.2021.3073632","volume":"25","author":"M Awais","year":"2021","unstructured":"Awais M, Long X, Yin B, Abbasi SF, Akbarzadeh S, Lu C, Wang X, Wang L, Zhang J, Dudink J et al (2021) A hybrid dcnn-svm model for classifying neonatal sleep and wake states based on facial expressions in video. IEEE J Biomed Health Inf 25(5):1441\u20131449","journal-title":"IEEE J Biomed Health Inf"},{"key":"1628_CR7","doi-asserted-by":"publisher","first-page":"183025","DOI":"10.1109\/ACCESS.2020.3028182","volume":"8","author":"SF Abbasi","year":"2020","unstructured":"Abbasi SF, Ahmad J, Tahir A, Awais M, Chen C, Irfan M, Siddiqa HA, Waqas AB, Long X, Yin B et al (2020) Eeg-based neonatal sleep-wake classification using multilayer perceptron neural network. IEEE Access 8:183025\u2013183034","journal-title":"IEEE Access"},{"key":"1628_CR8","first-page":"4619","volume":"70","author":"SF Abbasi","year":"2022","unstructured":"Abbasi SF, Jamil H, Chen W (2022) Eeg-based neonatal sleep stage classification using ensemble learning. Comput Mater Contin 70:4619\u20134633","journal-title":"Comput Mater Contin"},{"key":"1628_CR9","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples. In: Proceedings of international conference on learning representations (ICLR)"},{"key":"1628_CR10","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 (ICLR)"},{"key":"1628_CR11","doi-asserted-by":"crossref","unstructured":"Zhu P, Pan Z, Tang K, Cui X, Wang J, Xuan Q (2024) Node injection attack based on label propagation against graph neural network. IEEE Trans Comput Soc Syst","DOI":"10.1109\/TCSS.2024.3395794"},{"key":"1628_CR12","doi-asserted-by":"crossref","unstructured":"Zhang J, Huang J-T, Wang W, Li Y, Wu W, Wang X, Su Y, Lyu MR (2023) Improving the transferability of adversarial samples by path-augmented method. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 8173\u20138182","DOI":"10.1109\/CVPR52729.2023.00790"},{"key":"1628_CR13","doi-asserted-by":"crossref","unstructured":"Zhu P, Hong J, Li X, Tang K, Wang Z (2023) SGMA: a novel adversarial attack approach with improved transferability. Complex Intell Syst 1\u201313","DOI":"10.1007\/s40747-023-01060-0"},{"key":"1628_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103135","volume":"128","author":"X He","year":"2023","unstructured":"He X, Li Y, Qu H, Dong J (2023) Improving transferable adversarial attack via feature-momentum. Comput Secur 128:103135","journal-title":"Comput Secur"},{"key":"1628_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109037","volume":"133","author":"Y Bai","year":"2023","unstructured":"Bai Y, Wang Y, Zeng Y, Jiang Y, Xia S-T (2023) Query efficient black-box adversarial attack on deep neural networks. Pattern Recogn 133:109037","journal-title":"Pattern Recogn"},{"key":"1628_CR16","doi-asserted-by":"crossref","unstructured":"Li Q, Li X, Cui X, Tang K, Zhu P (2023) HEPT Attack: Heuristic Perpendicular Trial for Hard-label Attacks under Limited Query Budgets. In: The 32nd ACM International Conference on Information and Knowledge Management (CIKM)","DOI":"10.1145\/3583780.3615198"},{"key":"1628_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103482","volume":"135","author":"H Dong","year":"2023","unstructured":"Dong H, Dong J, Wan S, Yuan S, Guan Z (2023) Transferable adversarial distribution learning: query-efficient adversarial attack against large language models. Comput Secur 135:103482","journal-title":"Comput Secur"},{"key":"1628_CR18","doi-asserted-by":"crossref","unstructured":"Wang X, He K (2021) Enhancing the transferability of adversarial attacks through variance tuning. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1924\u20131933","DOI":"10.1109\/CVPR46437.2021.00196"},{"key":"1628_CR19","doi-asserted-by":"crossref","unstructured":"Jang D, Son S, Kim D-S (2022) Strengthening the transferability of adversarial examples using advanced looking ahead and self-cutmix. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 148\u2013155","DOI":"10.1109\/CVPRW56347.2022.00026"},{"key":"1628_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103674","volume":"139","author":"P Zhu","year":"2024","unstructured":"Zhu P, Fan Z, Guo S, Tang K, Li X (2024) Improving adversarial transferability through hybrid augmentation. Comput Secur 139:103674","journal-title":"Comput Secur"},{"key":"1628_CR21","doi-asserted-by":"crossref","unstructured":"Dong Y, Liao F, Pang T, Su H, Zhu J, Hu X, Li J (2019) Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 9185\u20139193","DOI":"10.1109\/CVPR.2018.00957"},{"key":"1628_CR22","unstructured":"Lin J, Song C, He K, Wang L, Hopcroft JE (2020) Nesterov accelerated gradient and scale invariance for adversarial attacks. In: International Conference on Learning Representations (ICLR)"},{"key":"1628_CR23","unstructured":"Wang X, Lin J, Hu H, Wang J, He K (2021) Boosting adversarial transferability through enhanced momentum. In: British Machine Vision Conference (BMVC)"},{"key":"1628_CR24","doi-asserted-by":"crossref","unstructured":"Xie C, Zhang Z, Zhou Y, Bai S, Wang J, Ren Z, Alan Y (2019) Improving transferability of adversarial examples with input diversity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2730\u20132739","DOI":"10.1109\/CVPR.2019.00284"},{"key":"1628_CR25","doi-asserted-by":"crossref","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 Conference on Computer Vision and Pattern Recognition (CVPR), pp 4312\u20134321","DOI":"10.1109\/CVPR.2019.00444"},{"key":"1628_CR26","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang J, He K (2021) Admix: enhancing the transferability of adversarial attacks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 16138\u201316147","DOI":"10.1109\/ICCV48922.2021.01585"},{"key":"1628_CR27","doi-asserted-by":"crossref","unstructured":"Hong J, Tang K, Gao C, Wang S, Guo S, Zhu P (2022) GM-attack: improving the transferability of adversarial attacks. In: 2022 International Conference on Knowledge Science, Engineering and Management (KSEM), pp 489\u2013500","DOI":"10.1007\/978-3-031-10989-8_39"},{"key":"1628_CR28","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Cision (ICCV), pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"key":"1628_CR29","doi-asserted-by":"crossref","unstructured":"Kurakin A, Goodfellow IJ, Bengio S (2017) Adversarial examples in the physical world. In: Proceedings of International Conference on Learning Representations (ICLR)","DOI":"10.1201\/9781351251389-8"},{"key":"1628_CR30","doi-asserted-by":"crossref","unstructured":"Zhu H, Ren Y, Sui X, Yang L, Jiang W (2023) Boosting adversarial transferability via gradient relevance attack. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 4741\u20134750","DOI":"10.1109\/ICCV51070.2023.00437"},{"key":"1628_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119918","volume":"657","author":"S Guo","year":"2024","unstructured":"Guo S, Li X, Zhu P, Wang B, Mu Z, Zhao J (2024) Mixcam-attack: boosting the transferability of adversarial examples with targeted data augmentation. Inf Sci 657:119918","journal-title":"Inf Sci"},{"key":"1628_CR32","doi-asserted-by":"crossref","unstructured":"Wang K, He X, Wang W, Wang X (2024) Boosting adversarial transferability by block shuffle and rotation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 24336\u201324346","DOI":"10.1109\/CVPR52733.2024.02297"},{"key":"1628_CR33","unstructured":"Liu Y, Chen X, Liu C, Song D (2017) Delving into transferable adversarial examples and black-box attacks. In: International Conference on Learning Representations (ICLR)"},{"key":"1628_CR34","doi-asserted-by":"crossref","unstructured":"Li Y, Bai S, Zhou Y, Xie C, Zhang Z, Yuille A (2020) Learning transferable adversarial examples via ghost networks. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, pp 11458\u201311465","DOI":"10.1609\/aaai.v34i07.6810"},{"key":"1628_CR35","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.neunet.2022.02.025","volume":"150","author":"L Hao","year":"2022","unstructured":"Hao L, Hao K, Wei B, Tang X-S (2022) Boosting the transferability of adversarial examples via stochastic serial attack. Neural Netw 150:58\u201367","journal-title":"Neural Netw"},{"key":"1628_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107184","volume":"101","author":"J Hang","year":"2020","unstructured":"Hang J, Han K, Chen H, Li Y (2020) Ensemble adversarial black-box attacks against deep learning systems. Pattern Recogn 101:107184","journal-title":"Pattern Recogn"},{"key":"1628_CR37","unstructured":"Tram\u00e9r F, Kurakin A, Papernot N, Goodfellow I, Boneh D, McDaniel P (2018) Ensemble adversarial training: attacks and defenses. In: International Conference on Learning Representations (ICLR)"},{"key":"1628_CR38","unstructured":"Xie C, Wang J, Zhang Z, Ren Z, Yuille A (2017) Mitigating adversarial effects through randomization. In: International Conference on Learning Representations (ICLR)"},{"key":"1628_CR39","doi-asserted-by":"crossref","unstructured":"Liu Z, Liu Q, Liu T, Xu N, Lin X, Wang Y, Wen W (2019) Feature distillation: dnn-oriented jpeg compression against adversarial examples. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 860\u2013868","DOI":"10.1109\/CVPR.2019.00095"},{"key":"1628_CR40","unstructured":"Cohen J, Rosenfeld E, Kolter Z (2019) Certified adversarial robustness via randomized smoothing. In: International Conference on Machine Learning, pp 1310\u20131320"},{"key":"1628_CR41","doi-asserted-by":"crossref","unstructured":"Liao F, Liang M, Dong Y, Pang T, Hu X, Zhu J (2018) Defense against adversarial attacks using high-level representation guided denoiser. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1778\u20131787","DOI":"10.1109\/CVPR.2018.00191"},{"key":"1628_CR42","doi-asserted-by":"crossref","unstructured":"Jia X, Wei X, Cao X, Foroosh H (2019) Comdefend: an efficient image compression model to defend adversarial examples. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 6084\u20136092","DOI":"10.1109\/CVPR.2019.00624"},{"key":"1628_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110388","volume":"265","author":"S Guo","year":"2023","unstructured":"Guo S, Li X, Zhu P, Mu Z (2023) ADS-detector: an attention-based dual stream adversarial example detection method. Knowl-Based Syst 265:110388","journal-title":"Knowl-Based Syst"},{"key":"1628_CR44","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, Inception-resnet and the impact of residual connections on learning. In: Proceedings of AAAI Conference on Artificial Intelligence, pp 4278\u20134284","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"1628_CR45","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Sergey I, Jon S, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"1628_CR46","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 (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01628-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01628-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01628-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:16:50Z","timestamp":1738268210000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01628-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,9]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1628"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01628-4","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,9]]},"assertion":[{"value":"29 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 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":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"4"}}