{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T05:36:14Z","timestamp":1741066574068,"version":"3.38.0"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key R&D Program of Jiangsu Province, China","award":["BE2023010-3","BE2023010-3","BE2023010-3"],"award-info":[{"award-number":["BE2023010-3","BE2023010-3","BE2023010-3"]}]},{"name":"ZTE Industry-University-Institute Cooperation Funds","award":["HC-CN-20221107001","HC-CN-20221107001"],"award-info":[{"award-number":["HC-CN-20221107001","HC-CN-20221107001"]}]},{"name":"National Science Foundation","award":["2144772"],"award-info":[{"award-number":["2144772"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00371-024-03548-3","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T13:01:56Z","timestamp":1720702916000},"page":"2495-2510","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ACL-SAR: model agnostic adversarial contrastive learning for robust skeleton-based action recognition"],"prefix":"10.1007","volume":"41","author":[{"given":"Jiaxuan","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Ming","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Libo","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Siyu","family":"Xia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"issue":"1","key":"3548_CR1","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2019","unstructured":"Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y.: Openpose: realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172\u2013186 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3548_CR2","doi-asserted-by":"crossref","unstructured":"Fang, H.-S., Xie, S., Tai, Y.-W.: RMPE: regional multi-person pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision vol. 2334, 2343 (2017)","DOI":"10.1109\/ICCV.2017.256"},{"key":"3548_CR3","doi-asserted-by":"crossref","unstructured":"G\u00fcler, R.A., Neverova, N., Kokkinos, I.: Densepose: dense human pose estimation in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7297\u20137306 (2018)","DOI":"10.1109\/CVPR.2018.00762"},{"key":"3548_CR4","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"3548_CR5","doi-asserted-by":"crossref","unstructured":"Du, Y., Fu, Y., Wang, L.: Skeleton based action recognition with convolutional neural network. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), IEEE, pp. 579\u2013583 (2015)","DOI":"10.1109\/ACPR.2015.7486569"},{"key":"3548_CR6","doi-asserted-by":"crossref","unstructured":"Du, Y., Wang, W., Wang, L.: Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110\u20131118 (2015)","DOI":"10.1109\/CVPR.2015.7298714"},{"key":"3548_CR7","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, P., Wang, S., Hou, Y., Li, W.: Skeleton-based action recognition using LSTM and CNN. In: 2017 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), IEEE, pp. 585\u2013590 (2017)","DOI":"10.1109\/ICMEW.2017.8026287"},{"key":"3548_CR8","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3595\u20133603 (2019)","DOI":"10.1109\/CVPR.2019.00371"},{"key":"3548_CR9","unstructured":"Xu, B., Shu, X., Zhang, J., Dai, G., Song, Y.: Spatiotemporal decouple-and-squeeze contrastive learning for semisupervised skeleton-based action recognition. IEEE Trans. Neural Netw. Learn. Syst"},{"key":"3548_CR10","unstructured":"Xu, B., Shu, X.: Pyramid self-attention polymerization learning for semi-supervised skeleton-based action recognition, arXiv preprint arXiv:2302.02327"},{"key":"3548_CR11","unstructured":"Shu, X., Xu, B., Zhang, L., Tang, J.: Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition. IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"3548_CR12","doi-asserted-by":"publisher","first-page":"3852","DOI":"10.1109\/TIP.2022.3175605","volume":"31","author":"B Xu","year":"2022","unstructured":"Xu, B., Shu, X., Song, Y.: X-invariant contrastive augmentation and representation learning for semi-supervised skeleton-based action recognition. IEEE Trans. Image Process. 31, 3852\u20133867 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"3548_CR13","unstructured":"Tu, Z., Zhang, J., Li, H., Chen, Y., Yuan, J.: Joint-bone fusion graph convolutional network for semi-supervised skeleton action recognition. IEEE Trans. Multimedia"},{"issue":"12","key":"3548_CR14","doi-asserted-by":"publisher","first-page":"8646","DOI":"10.1109\/TCSVT.2022.3193574","volume":"32","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Jia, Y., Xie, W., Tu, Z.: Zoom transformer for skeleton-based group activity recognition. IEEE Trans. Circuits Syst. Video Technol. 32(12), 8646\u20138659 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"3548_CR15","unstructured":"Guan, S., Yu, X., Huang, W., Fang, G., Lu, H.: Dmmg: Dual min-max games for self-supervised skeleton-based action recognition, arXiv preprint arXiv:2302.12007"},{"key":"3548_CR16","doi-asserted-by":"crossref","unstructured":"Zhou, H., Liu, Q., Wang, Y.: Learning discriminative representations for skeleton based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10608\u201310617 (2023)","DOI":"10.1109\/CVPR52729.2023.01022"},{"key":"3548_CR17","doi-asserted-by":"crossref","unstructured":"Si, C., Chen, W., Wang, W., Wang, L., Tan, T.: An attention enhanced graph convolutional LSTM network for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1227\u20131236 (2019)","DOI":"10.1109\/CVPR.2019.00132"},{"key":"3548_CR18","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7912\u20137921 (2019)","DOI":"10.1109\/CVPR.2019.00810"},{"key":"3548_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 143\u2013152 (2020)","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"3548_CR20","doi-asserted-by":"crossref","unstructured":"Lin, L., Song, S., Yang, W., Liu, J.: Ms2l: Multi-task self-supervised learning for skeleton based action recognition. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2490\u20132498 (2020)","DOI":"10.1145\/3394171.3413548"},{"key":"3548_CR21","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.ins.2021.04.023","volume":"569","author":"H Rao","year":"2021","unstructured":"Rao, H., Xu, S., Hu, X., Cheng, J., Hu, B.: Augmented skeleton based contrastive action learning with momentum LSTM for unsupervised action recognition. Inf. Sci. 569, 90\u2013109 (2021)","journal-title":"Inf. Sci."},{"key":"3548_CR22","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, M., Ni, B., Wang, H., Yang, J., Zhang, W.: 3d human action representation learning via cross-view consistency pursuit. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4741\u20134750 (2021)","DOI":"10.1109\/CVPR46437.2021.00471"},{"key":"3548_CR23","doi-asserted-by":"crossref","unstructured":"Wang, H., He, F., Peng, Z., Shao, T., Yang, Y.-L., Zhou, K., Hogg, D.: Understanding the robustness of skeleton-based action recognition under adversarial attack. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14656\u201314665 (2021)","DOI":"10.1109\/CVPR46437.2021.01442"},{"key":"3548_CR24","unstructured":"Liu, J., Akhtar, N., Mian, A.: Adversarial attack on skeleton-based human action recognition. IEEE Trans Neural Netw Learn Syst"},{"key":"3548_CR25","doi-asserted-by":"crossref","unstructured":"Diao, Y., Shao, T., Yang, Y.-L., Zhou, K., Wang, H.: Basar: Black-box attack on skeletal action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7597\u20137607 (2021)","DOI":"10.1109\/CVPR46437.2021.00751"},{"key":"3548_CR26","doi-asserted-by":"crossref","unstructured":"Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., Wang, Z.: Adversarial robustness: From self-supervised pre-training to fine-tuning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 699\u2013708 (2020)","DOI":"10.1109\/CVPR42600.2020.00078"},{"key":"3548_CR27","unstructured":"Kim, M., Tack, J., Hwang, S.J.: Adversarial self-supervised contrastive learning. In: Advances in Neural Information Processing Systems 33"},{"key":"3548_CR28","unstructured":"Ho, C.-H., Vasconcelos, N.: Contrastive learning with adversarial examples, arXiv preprint arXiv:2010.12050"},{"key":"3548_CR29","unstructured":"Jiang, Z., Chen, T., Chen, T., Wang, Z.: Robust pre-training by adversarial contrastive learning.. In: NeurIPS (2020)"},{"issue":"2","key":"3548_CR30","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/MMUL.2012.24","volume":"19","author":"Z Zhang","year":"2012","unstructured":"Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia 19(2), 4\u201310 (2012)","journal-title":"IEEE Multimedia"},{"key":"3548_CR31","doi-asserted-by":"crossref","unstructured":"Shahroudy, A., Liu, J., Ng, T.-T., Wang, G.: Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010\u20131019 (2016)","DOI":"10.1109\/CVPR.2016.115"},{"key":"3548_CR32","unstructured":"M\u00fcller, M., R\u00f6der, T., Clausen, M., Eberhardt, B., Kr\u00fcger, B., Weber, A.: Documentation mocap database hdm05"},{"key":"3548_CR33","doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Arrate, F., Chellappa, R.: Human action recognition by representing 3d skeletons as points in a lie group. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 588\u2013595 (2014)","DOI":"10.1109\/CVPR.2014.82"},{"key":"3548_CR34","doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Chellapa, R.: Rolling rotations for recognizing human actions from 3d skeletal data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4471\u20134479 (2016)","DOI":"10.1109\/CVPR.2016.484"},{"key":"3548_CR35","doi-asserted-by":"crossref","unstructured":"Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30, (2016)","DOI":"10.1609\/aaai.v30i1.10451"},{"issue":"12","key":"3548_CR36","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.1109\/TPAMI.2017.2771306","volume":"40","author":"J Liu","year":"2017","unstructured":"Liu, J., Shahroudy, A., Xu, D., Kot, A.C., Wang, G.: Skeleton-based action recognition using spatio-temporal LSTM network with trust gates. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 3007\u20133021 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3548_CR37","doi-asserted-by":"crossref","unstructured":"Lee, I., Kim, D., Kang, S., Lee, S.: Ensemble deep learning for skeleton-based action recognition using temporal sliding LSTM networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1012\u20131020 (2017)","DOI":"10.1109\/ICCV.2017.115"},{"key":"3548_CR38","unstructured":"Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization, arXiv preprint arXiv:1409.2329"},{"key":"3548_CR39","unstructured":"Liu, H., Tu, J., Liu, M.: Two-stream 3d convolutional neural network for skeleton-based action recognition, arXiv preprint arXiv:1705.08106"},{"issue":"17","key":"3548_CR40","doi-asserted-by":"publisher","first-page":"22901","DOI":"10.1007\/s11042-018-5642-0","volume":"77","author":"B Li","year":"2018","unstructured":"Li, B., He, M., Dai, Y., Cheng, X., Chen, Y.: 3d skeleton based action recognition by video-domain translation-scale invariant mapping and multi-scale dilated CNN. Multimedia Tools and Applications 77(17), 22901\u201322921 (2018)","journal-title":"Multimedia Tools and Applications"},{"issue":"11","key":"3548_CR41","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1109\/TMM.2019.2962304","volume":"22","author":"K Zhu","year":"2019","unstructured":"Zhu, K., Wang, R., Zhao, Q., Cheng, J., Tao, D.: A cuboid CNN model with an attention mechanism for skeleton-based action recognition. IEEE Trans. Multimedia 22(11), 2977\u20132989 (2019)","journal-title":"IEEE Trans. Multimedia"},{"key":"3548_CR42","doi-asserted-by":"crossref","unstructured":"Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., Lu, H.: Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 183\u2013192 (2020)","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"3548_CR43","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12026\u201312035 (2019)","DOI":"10.1109\/CVPR.2019.01230"},{"key":"3548_CR44","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv:1609.02907"},{"key":"3548_CR45","doi-asserted-by":"crossref","unstructured":"Lea, C., Flynn, M.D., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks for action segmentation and detection. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 156\u2013165 (2017)","DOI":"10.1109\/CVPR.2017.113"},{"key":"3548_CR46","doi-asserted-by":"crossref","unstructured":"Su, K., Liu, X., Shlizerman, E.: Predict and cluster: Unsupervised skeleton based action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9631\u20139640 (2020)","DOI":"10.1109\/CVPR42600.2020.00965"},{"key":"3548_CR47","doi-asserted-by":"crossref","unstructured":"Zheng, N., Wen, J., Liu, R., Long, L., Dai, J., Gong, Z.: Unsupervised representation learning with long-term dynamics for skeleton based action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32, (2018)","DOI":"10.1609\/aaai.v32i1.11853"},{"key":"3548_CR48","unstructured":"Li, J., Shlizerman, E.: Sparse semi-supervised action recognition with active learning, arXiv preprint arXiv:2012.01740"},{"key":"3548_CR49","unstructured":"Xu, S., Rao, H., Hu, X., Cheng, J., Hu, B.: Prototypical contrast and reverse prediction: unsupervised skeleton based action recognition. IEEE Trans. Multimedia"},{"key":"3548_CR50","doi-asserted-by":"crossref","unstructured":"Barsoum, E., Kender, J., Liu, Z.: Hp-gan: Probabilistic 3d human motion prediction via GAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1418\u20131427 (2018)","DOI":"10.1109\/CVPRW.2018.00191"},{"key":"3548_CR51","doi-asserted-by":"crossref","unstructured":"Yao, H., Zhao, S., Xie, C., Ye, K., Liang, S.: Recurrent graph convolutional autoencoder for unsupervised skeleton-based action recognition. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 1\u20136 (2021)","DOI":"10.1109\/ICME51207.2021.9428403"},{"key":"3548_CR52","unstructured":"Li, J., Shlizerman, E.: Iterate & cluster: Iterative semi-supervised action recognition, arXiv preprint arXiv:2006.06911"},{"key":"3548_CR53","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"3548_CR54","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, PMLR, pp. 1597\u20131607 (2020)"},{"key":"3548_CR55","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks, arXiv preprint arXiv:1312.6199"},{"key":"3548_CR56","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.:L Explaining and harnessing adversarial examples, arXiv preprint arXiv:1412.6572"},{"key":"3548_CR57","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1765\u20131773 (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"3548_CR58","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks, arXiv preprint arXiv:1706.06083"},{"issue":"13","key":"3548_CR59","doi-asserted-by":"publisher","first-page":"11365","DOI":"10.1109\/JIOT.2021.3128646","volume":"9","author":"G Sun","year":"2021","unstructured":"Sun, G., Cong, Y., Dong, J., Wang, Q., Lyu, L., Liu, J.: Data poisoning attacks on federated machine learning. IEEE Internet Things J. 9(13), 11365\u201311375 (2021)","journal-title":"IEEE Internet Things J."},{"key":"3548_CR60","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pp. 506\u2013519 (2017)","DOI":"10.1145\/3052973.3053009"},{"key":"3548_CR61","unstructured":"Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks, arXiv preprint arXiv:1611.02770"},{"issue":"5","key":"3548_CR62","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","volume":"23","author":"J Su","year":"2019","unstructured":"Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evol. Comput. 23(5), 828\u2013841 (2019)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"3548_CR63","unstructured":"Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., Song, D.: Generating adversarial examples with adversarial networks, arXiv preprint arXiv:1801.02610"},{"key":"3548_CR64","doi-asserted-by":"crossref","unstructured":"Tanaka, N., Kera, H., Kawamoto, K.: Adversarial bone length attack on action recognition. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, AAAI Press, 2022, pp. 2335\u20132343. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/20132","DOI":"10.1609\/aaai.v36i2.20132"},{"key":"3548_CR65","doi-asserted-by":"crossref","unstructured":"Lu, Z., Wang, H., Chang, Z., Yang, G., Shum, H.P.: Hard no-box adversarial attack on skeleton-based human action recognition with skeleton-motion-informed gradient. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4597\u20134606 (2023)","DOI":"10.1109\/ICCV51070.2023.00424"},{"key":"3548_CR66","unstructured":"Feinman, R., Curtin, R.R., Shintre, S., Gardner, A.B.: Detecting adversarial samples from artifacts, arXiv preprint arXiv:1703.00410"},{"key":"3548_CR67","doi-asserted-by":"crossref","unstructured":"Lu, J., Issaranon, T., Forsyth, D.: Safetynet: Detecting and rejecting adversarial examples robustly. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 446\u2013454 (2017)","DOI":"10.1109\/ICCV.2017.56"},{"key":"3548_CR68","doi-asserted-by":"crossref","unstructured":"Cohen, G., Sapiro, G., Giryes, R.: Detecting adversarial samples using influence functions and nearest neighbors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14453\u201314462 (2020)","DOI":"10.1109\/CVPR42600.2020.01446"},{"key":"3548_CR69","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Zhang, R., Wu, B., Li, W., Mo, T.: Detection by attack: detecting adversarial samples by undercover attack. In: European Symposium on Research in Computer Security, pp. 146\u2013164. Springer, Berlin (2020)","DOI":"10.1007\/978-3-030-59013-0_8"},{"key":"3548_CR70","doi-asserted-by":"crossref","unstructured":"Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., Zhu, J.: Defense against adversarial attacks using high-level representation guided denoiser. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1778\u20131787 (2018)","DOI":"10.1109\/CVPR.2018.00191"},{"key":"3548_CR71","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1109\/TIP.2019.2940533","volume":"29","author":"A Mustafa","year":"2019","unstructured":"Mustafa, A., Khan, S.H., Hayat, M., Shen, J., Shao, L.: Image super-resolution as a defense against adversarial attacks. IEEE Trans. Image Process. 29, 1711\u20131724 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"3548_CR72","unstructured":"Shafahi, A., Najibi, M., Ghiasi, A., Xu, Z., Dickerson, J., Studer, C., Davis, L.S., Taylor, G., Goldstein, T.: Adversarial training for free! arXiv preprint arXiv:1904.12843"},{"key":"3548_CR73","unstructured":"Tram\u00e8r, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., McDaniel, P.: Ensemble adversarial training: attacks and defenses, arXiv preprint arXiv:1705.07204"},{"key":"3548_CR74","unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., Jordan, M.: Theoretically principled trade-off between robustness and accuracy. In: International Conference on Machine Learning, PMLR, pp. 7472\u20137482 (2019)"},{"key":"3548_CR75","unstructured":"Zhang, J., Xu, X., Han, B., Niu, G., Cui, L., Sugiyama, M., Kankanhalli, M.: Attacks which do not kill training make adversarial learning stronger. In: International Conference on Machine Learning, PMLR, pp. 11278\u201311287 (2020)"},{"key":"3548_CR76","unstructured":"Uesato, J., Alayrac, J.-B., Huang, P.-S., Stanforth, R., Fawzi, A., Kohli, P.: Are labels required for improving adversarial robustness? arXiv preprint arXiv:1905.13725"},{"key":"3548_CR77","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding, arXiv preprint arXiv:1807.03748"},{"key":"3548_CR78","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"3548_CR79","doi-asserted-by":"crossref","unstructured":"Cho, K., Chen, X.: Classifying and visualizing motion capture sequences using deep neural networks. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), IEEE vol. 2, pp. 122\u2013130 (2014)","DOI":"10.5220\/0004718301220130"},{"issue":"11","key":"3548_CR80","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03548-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03548-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03548-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T11:33:18Z","timestamp":1741001598000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03548-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,11]]},"references-count":80,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3548"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03548-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2024,7,11]]},"assertion":[{"value":"10 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2024","order":2,"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"}}]}}