{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T10:26:58Z","timestamp":1761388018974,"version":"build-2065373602"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ22F020007"],"award-info":[{"award-number":["LZ22F020007"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["92167203"],"award-info":[{"award-number":["92167203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity","award":["HIK2022008"],"award-info":[{"award-number":["HIK2022008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00530-025-01915-1","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T11:39:04Z","timestamp":1755776344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing transferability of targeted adversarial examples through amplitude spectrum alignment"],"prefix":"10.1007","volume":"31","author":[{"given":"Yaguan","family":"Qian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqiang","family":"Sha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoquan","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanchun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"1915_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2023.110451","volume":"269","author":"AR Shahid","year":"2023","unstructured":"Shahid, A.R., Yan, H.: Squeezexpnet: dual-stage convolutional neural network for accurate facial expression recognition with attention mechanism. Knowl. Based Syst. 269, 110451 (2023). https:\/\/doi.org\/10.1016\/J.KNOSYS.2023.110451","journal-title":"Knowl. Based Syst."},{"key":"1915_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2024.111522","volume":"289","author":"Y Lu","year":"2024","unstructured":"Lu, Y., Jiang, B., Liu, N., Li, Y., Chen, J., Zhang, Y., Wan, Z.: Crossprune: cooperative pruning for camera-lidar fused perception models of autonomous driving. Knowl. Based Syst. 289, 111522 (2024). https:\/\/doi.org\/10.1016\/J.KNOSYS.2024.111522","journal-title":"Knowl. Based Syst."},{"key":"1915_CR3","unstructured":"Gao, J., Chen, M., Xiang, L., Xu, C.: A comprehensive survey on evidential deep learning and its applications. arXiv preprint arXiv:2409.04720 (2024)"},{"key":"1915_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2022.110071","volume":"259","author":"S Zhong","year":"2023","unstructured":"Zhong, S., Scarinci, A., Cicirello, A.: Natural language processing for systems engineering: automatic generation of systems modelling language diagrams. Knowl. Based Syst. 259, 110071 (2023). https:\/\/doi.org\/10.1016\/J.KNOSYS.2022.110071","journal-title":"Knowl. Based Syst."},{"key":"1915_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/J.KNOSYS.2023.110475","volume":"268","author":"B Nagy","year":"2023","unstructured":"Nagy, B., Heged\u00fcs, I., S\u00e1ndor, N., Egedi, B., Mehmood, H., Saravanan, K., L\u00f3ki, G., Kiss, \u00c1.: Privacy-preserving federated learning and its application to natural language processing. Knowl. Based Syst. 268, 110475 (2023). https:\/\/doi.org\/10.1016\/J.KNOSYS.2023.110475","journal-title":"Knowl. Based Syst."},{"issue":"6","key":"1915_CR6","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.1109\/TPAMI.2025.3546312","volume":"47","author":"J Gao","year":"2025","unstructured":"Gao, J., Chen, M., Xu, C.: Learning probabilistic presence-absence evidence for weakly-supervised audio-visual event perception. IEEE Trans. Pattern Anal. Mach. Intell. 47(6), 4787\u20134802 (2025)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1915_CR7","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint (2013) arXiv:1312.6199"},{"key":"1915_CR8","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint (2015) arXiv:1412.6572"},{"key":"1915_CR9","unstructured":"Li, Q., Guo, Y., Chen, H.: Practical no-box adversarial attacks against dnns. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, pp. 12849\u201312860 (2020)"},{"key":"1915_CR10","doi-asserted-by":"publisher","unstructured":"Duan, R., Chen, Y., Niu, D., Yang, Y., Qin, A.K., He, Y.: Advdrop: adversarial attack to dnns by dropping information. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV, pp. 7486\u20137495. IEEE, Montreal (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00741","DOI":"10.1109\/ICCV48922.2021.00741"},{"key":"1915_CR11","first-page":"321","volume-title":"28th USENIX Security Symposium, USENIX Security 2019","author":"A Demontis","year":"2019","unstructured":"Demontis, A., Melis, M., Pintor, M., Jagielski, M., Biggio, B., Oprea, A., Nita-Rotaru, C., Roli, F.: Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks. In: Heninger, N., Traynor, P. (eds.) 28th USENIX Security Symposium, USENIX Security 2019, pp. 321\u2013338. USENIX Association, Santa Clara (2019)"},{"key":"1915_CR12","unstructured":"Liang, K., Zhang, J.Y., Wang, B., Yang, Z., Koyejo, S., Li, B.: Uncovering the connections between adversarial transferability and knowledge transferability. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021. Proceedings of Machine Learning Research, vol. 139, pp. 6577\u20136587. PMLR, Virtual (2021)"},{"key":"1915_CR13","doi-asserted-by":"publisher","unstructured":"Liu, Z., Luo, Y., Wu, L., Li, S., Liu, Z., Li, S.Z.: Are gradients on graph structure reliable in gray-box attacks? In: Hasan, M.A., Xiong, L. (eds.) Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 1360\u20131368. ACM, Atlanta (2022). https:\/\/doi.org\/10.1145\/3511808.3557238","DOI":"10.1145\/3511808.3557238"},{"key":"1915_CR14","unstructured":"Liu, Z., Luo, Y., Wu, L., Liu, Z., Li, S.Z.: Towards reasonable budget allocation in untargeted graph structure attacks via gradient debias. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans (2022)"},{"key":"1915_CR15","unstructured":"Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon (2017)"},{"key":"1915_CR16","doi-asserted-by":"publisher","unstructured":"Wang, X., He, X., Wang, J., He, K.: Admix: enhancing the transferability of adversarial attacks. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, pp. 16138\u201316147. IEEE, Montreal (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01585","DOI":"10.1109\/ICCV48922.2021.01585"},{"key":"1915_CR17","doi-asserted-by":"publisher","unstructured":"Wang, Z., Guo, H., Zhang, Z., Liu, W., Qin, Z., Ren, K.: Feature importance-aware transferable adversarial attacks. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, pp. 7619\u20137628. IEEE, Montreal (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00754","DOI":"10.1109\/ICCV48922.2021.00754"},{"key":"1915_CR18","unstructured":"Wu, D., Wang, Y., Xia, S., Bailey, J., Ma, X.: Skip connections matter: on the transferability of adversarial examples generated with resnets. In: 8th International Conference on Learning Representations, ICLR 2020. OpenReview.net, Addis Ababa (2020)"},{"key":"1915_CR19","doi-asserted-by":"publisher","unstructured":"Gao, L., Cheng, Y., Zhang, Q., Xu, X., Song, J.: Feature space targeted attacks by statistic alignment. In: Zhou, Z. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, pp. 671\u2013677. ijcai.org, Montreal (2021). https:\/\/doi.org\/10.24963\/IJCAI.2021\/93","DOI":"10.24963\/IJCAI.2021\/93"},{"key":"1915_CR20","doi-asserted-by":"publisher","unstructured":"Long, Y., Zhang, Q., Zeng, B., Gao, L., Liu, X., Zhang, J., Song, J.: Frequency domain model augmentation for adversarial attack. In: Avidan, S., Brostow, G.J., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv. Lecture Notes in Computer Science, vol. 13664, pp. 549\u2013566. Springer, Israel (2022). https:\/\/doi.org\/10.1007\/978-3-031-19772-7_32","DOI":"10.1007\/978-3-031-19772-7_32"},{"key":"1915_CR21","doi-asserted-by":"publisher","unstructured":"Liang, K., Xiao, B.: Styless: Boosting the transferability of adversarial examples. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, pp. 8163\u20138172. IEEE, Vancouver (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00789","DOI":"10.1109\/CVPR52729.2023.00789"},{"key":"1915_CR22","doi-asserted-by":"publisher","unstructured":"Yang, X., Dong, Y., Pang, T., Su, H., Zhu, J.: Boosting transferability of targeted adversarial examples via hierarchical generative networks. In: Avidan, S., Brostow, G.J., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv. Lecture Notes in Computer Science, vol. 13664, pp. 725\u2013742. Springer, Israel (2022). https:\/\/doi.org\/10.1007\/978-3-031-19772-7_42","DOI":"10.1007\/978-3-031-19772-7_42"},{"key":"1915_CR23","doi-asserted-by":"publisher","unstructured":"Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., Li, J.: Boosting adversarial attacks with momentum. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 9185\u20139193. Computer Vision Foundation \/ IEEE Computer Society, Salt Lake City (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00957","DOI":"10.1109\/CVPR.2018.00957"},{"key":"1915_CR24","doi-asserted-by":"publisher","unstructured":"Dong, Y., Pang, T., Su, H., Zhu, J.: Evading defenses to transferable adversarial examples by translation-invariant attacks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, pp. 4312\u20134321. Computer Vision Foundation \/ IEEE, Long Beach (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00444","DOI":"10.1109\/CVPR.2019.00444"},{"key":"1915_CR25","doi-asserted-by":"publisher","unstructured":"Xie, C., Zhang, Z., Zhou, Y., Bai, S., Wang, J., Ren, Z., Yuille, A.L.: Improving transferability of adversarial examples with input diversity. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, pp. 2730\u20132739. Computer Vision Foundation \/ IEEE, Long Beach (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00284","DOI":"10.1109\/CVPR.2019.00284"},{"key":"1915_CR26","doi-asserted-by":"publisher","unstructured":"Mopuri, K.R., Uppala, P.K., Babu, R.V.: Ask, acquire, and attack: Data-free UAP generation using class impressions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision - ECCV 2018 - 15th European Conference. Lecture Notes in Computer Science, vol. 11213, pp. 20\u201335. Springer, Munich (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_2","DOI":"10.1007\/978-3-030-01240-3_2"},{"key":"1915_CR27","doi-asserted-by":"publisher","unstructured":"Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, pp. 14509\u201314518. Computer Vision Foundation \/ IEEE, Seattle (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01453","DOI":"10.1109\/CVPR42600.2020.01453"},{"key":"1915_CR28","unstructured":"Naseer, M., Khan, S.H., Khan, M.H., Khan, F.S., Porikli, F.: Cross-domain transferability of adversarial perturbations. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, pp. 12885\u201312895 (2019)"},{"key":"1915_CR29","doi-asserted-by":"publisher","unstructured":"Naseer, M., Khan, S.H., Hayat, M., Khan, F.S., Porikli, F.: On generating transferable targeted perturbations. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, pp. 7688\u20137697. IEEE, Montreal (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00761","DOI":"10.1109\/ICCV48922.2021.00761"},{"key":"1915_CR30","doi-asserted-by":"publisher","unstructured":"Moosavi-Dezfooli, S., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 86\u201394. IEEE Computer Society, Honolulu (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.17","DOI":"10.1109\/CVPR.2017.17"},{"key":"1915_CR31","doi-asserted-by":"publisher","unstructured":"Xiao, C., Li, B., Zhu, J., He, W., Liu, M., Song, D.: Generating adversarial examples with adversarial networks. In: Lang, J. (ed.) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, pp. 3905\u20133911. ijcai.org, Stockholm (2018). https:\/\/doi.org\/10.24963\/IJCAI.2018\/543","DOI":"10.24963\/IJCAI.2018\/543"},{"key":"1915_CR32","doi-asserted-by":"publisher","unstructured":"Poursaeed, O., Katsman, I., Gao, B., Belongie, S.J.: Generative adversarial perturbations. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 4422\u20134431. Computer Vision Foundation \/ IEEE Computer Society, Salt Lake City (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00465","DOI":"10.1109\/CVPR.2018.00465"},{"key":"1915_CR33","unstructured":"Yin, D., Gontijo\u00a0Lopes, R., Shlens, J., Cubuk, E.D., Gilmer, J.: A fourier perspective on model robustness in computer vision. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"1915_CR34","doi-asserted-by":"crossref","unstructured":"Chen, G., Peng, P., Ma, L., Li, J., Du, L., Tian, Y.: Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 458\u2013467 (2021)","DOI":"10.1109\/ICCV48922.2021.00051"},{"key":"1915_CR35","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"1915_CR36","doi-asserted-by":"publisher","unstructured":"Ganeshan, A., S., V.B., Radhakrishnan, V.B.: FDA: feature disruptive attack. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, pp. 8068\u20138078. IEEE, Seoul (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00816","DOI":"10.1109\/ICCV.2019.00816"},{"key":"1915_CR37","doi-asserted-by":"publisher","unstructured":"Zhang, J., Wu, W., Huang, J., Huang, Y., Wang, W., Su, Y., Lyu, M.R.: Improving adversarial transferability via neuron attribution-based attacks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, pp. 14973\u201314982. IEEE, New Orleans (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01457","DOI":"10.1109\/CVPR52688.2022.01457"},{"key":"1915_CR38","doi-asserted-by":"publisher","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D.J., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision\u2014ECCV 2014 - 13th European Conference. Lecture Notes in Computer Science, vol. 8689, pp. 818\u2013833. Springer, Zurich (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"1915_CR39","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp. 3320\u20133328 (2014)"},{"key":"1915_CR40","doi-asserted-by":"publisher","unstructured":"Yang, Y., Lao, D., Sundaramoorthi, G., Soatto, S.: Phase consistent ecological domain adaptation. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, pp. 9008\u20139017. Computer Vision Foundation \/ IEEE, Seattle (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00903","DOI":"10.1109\/CVPR42600.2020.00903"},{"key":"1915_CR41","doi-asserted-by":"publisher","unstructured":"Liu, Q., Chen, C., Qin, J., Dou, Q., Heng, P.: Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, pp. 1013\u20131023. Computer Vision Foundation \/ IEEE, Virtual (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00107","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"1915_CR42","doi-asserted-by":"publisher","unstructured":"Cai, M., Zhang, H., Huang, H., Geng, Q., Li, Y., Huang, G.: Frequency domain image translation: More photo-realistic, better identity-preserving. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, pp. 13910\u201313920. IEEE, Montreal (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01367","DOI":"10.1109\/ICCV48922.2021.01367"},{"key":"1915_CR43","unstructured":"Yang, M., Wang, Z., Chi, Z., Zhang, Y.: Fregan: Exploiting frequency components for training gans under limited data. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 (2022)"},{"key":"1915_CR44","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 248\u2013255. IEEE Computer Society, Miami (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1915_CR45","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 770\u2013778. IEEE Computer Society, Las Vegas (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"1915_CR46","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 2261\u20132269. IEEE Computer Society, Honolulu (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"1915_CR47","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015). http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"1915_CR48","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015)"},{"key":"1915_CR49","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.: Ensemble adversarial black-box attacks against deep learning systems. Pattern Recognit. 101, 107184 (2020). https:\/\/doi.org\/10.1016\/J.PATCOG.2019.107184","journal-title":"Pattern Recognit."},{"key":"1915_CR50","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In: 7th International Conference on Learning Representations, ICLR 2019. OpenReview.net, New Orleans (2019)"},{"key":"1915_CR51","unstructured":"Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: a simple data processing method to improve robustness and uncertainty. In: 8th International Conference on Learning Representations, ICLR 2020. OpenReview.net, Addis Ababa (2020)"},{"key":"1915_CR52","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Wilson, R.C., Hancock, E.R., Smith, W.A.P. (eds.) Proceedings of the British Machine Vision Conference 2016, BMVC 2016. BMVA Press, York (2016)","DOI":"10.5244\/C.30.87"},{"key":"1915_CR53","doi-asserted-by":"publisher","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 4510\u20134520. Computer Vision Foundation \/ IEEE Computer Society, Salt Lake City (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1915_CR54","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp. 1\u20139. IEEE Computer Society, Boston (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1915_CR55","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: 9th International Conference on Learning Representations, ICLR 2021. OpenReview.net, Austria (2021)"},{"key":"1915_CR56","unstructured":"Gao, L., Zhang, Q., Song, J., Shen, H.T.: Patch-wise++ perturbation for adversarial targeted attacks. arXiv preprint (2020) arXiv:1312.6199"},{"key":"1915_CR57","unstructured":"Shao, R., Shi, Z., Yi, J., Chen, P.-Y., Hsieh, C.-J.: On the adversarial robustness of vision transformers. arXiv preprint arXiv:2103.15670 (2021)"},{"key":"1915_CR58","doi-asserted-by":"crossref","unstructured":"Benz, P., Ham, S., Zhang, C., Karjauv, A., Kweon, I.S.: Adversarial robustness comparison of vision transformer and mlp-mixer to cnns. arXiv preprint arXiv:2110.02797 (2021)","DOI":"10.5244\/C.35.68"},{"key":"1915_CR59","doi-asserted-by":"crossref","unstructured":"Kim, G., Kim, J., Lee, J.-S.: Exploring adversarial robustness of vision transformers in the spectral perspective. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3976\u20133985 (2024)","DOI":"10.1109\/WACV57701.2024.00393"},{"key":"1915_CR60","unstructured":"Dziugaite, G.K., Ghahramani, Z., Roy, D.M.: A study of the effect of JPG compression on adversarial images. arXiv preprint (2016) arXiv:1608.00853"},{"key":"1915_CR61","unstructured":"Ding, G.W., Wang, L., Jin, X.: advertorch v0.1: an adversarial robustness toolbox based on pytorch. arXiv preprint (2019) arXiv:1902.07623"},{"key":"1915_CR62","doi-asserted-by":"publisher","unstructured":"Wang, H., Wu, X., Huang, Z., Xing, E.P.: High-frequency component helps explain the generalization of convolutional neural networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, pp. 8681\u20138691. Computer Vision Foundation \/ IEEE, Seattle (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00871","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"1915_CR63","unstructured":"Guo, C., Frank, J.S., Weinberger, K.Q.: Low frequency adversarial perturbation. In: Globerson, A., Silva, R. (eds.) Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019. Proceedings of Machine Learning Research, vol. 115, pp. 1127\u20131137. AUAI Press, Tel Aviv (2019)"},{"key":"1915_CR64","doi-asserted-by":"publisher","unstructured":"Sharma, Y., Ding, G.W., Brubaker, M.A.: On the effectiveness of low frequency perturbations. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pp. 3389\u20133396. ijcai.org, Macao (2019). https:\/\/doi.org\/10.24963\/IJCAI.2019\/470","DOI":"10.24963\/IJCAI.2019\/470"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01915-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01915-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01915-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T10:24:58Z","timestamp":1761387898000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01915-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":64,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["1915"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01915-1","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2025,8,21]]},"assertion":[{"value":"11 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2025","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}}],"article-number":"345"}}