{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:00:59Z","timestamp":1772643659615,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276031"],"award-info":[{"award-number":["62276031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276025"],"award-info":[{"award-number":["62276025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62206022"],"award-info":[{"award-number":["62206022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009592","name":"Beijing Municipal Science & Technology Commission","doi-asserted-by":"crossref","award":["Z231100007423015"],"award-info":[{"award-number":["Z231100007423015"]}],"id":[{"id":"10.13039\/501100009592","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen Technology Plan Program","award":["KQTD20170331093217368"],"award-info":[{"award-number":["KQTD20170331093217368"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s11263-024-02225-1","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T17:01:45Z","timestamp":1728579705000},"page":"1549-1563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Edge-Oriented Adversarial Attack for Deep Gait Recognition"],"prefix":"10.1007","volume":"133","author":[{"given":"Saihui","family":"Hou","sequence":"first","affiliation":[]},{"given":"Zengbin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Man","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chunshui","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4389-9805","authenticated-orcid":false,"given":"Yongzhen","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"issue":"4","key":"2225_CR1","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1109\/TBIOM.2020.3008862","volume":"2","author":"W An","year":"2020","unstructured":"An, W., Yu, S., Makihara, Y., Wu, X., Xu, C., Yu, Y., Liao, R., & Yagi, Y. (2020). Performance evaluation of model-based gait on multi-view very large population database with pose sequences. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2(4), 421\u2013430. https:\/\/doi.org\/10.1109\/TBIOM.2020.3008862","journal-title":"IEEE Transactions on Biometrics, Behavior, and Identity Science"},{"key":"2225_CR2","doi-asserted-by":"crossref","unstructured":"Arnab, A., Miksik, O., & Torr, P. H. (2018). On the robustness of semantic segmentation models to adversarial attacks. in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 888\u2013897.","DOI":"10.1109\/CVPR.2018.00099"},{"issue":"6","key":"2225_CR3","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1109\/TPAMI.2020.3031625","volume":"43","author":"S Bai","year":"2020","unstructured":"Bai, S., Li, Y., Zhou, Y., Li, Q., & Torr, P. H. (2020). Adversarial metric attack and defense for person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6), 2119\u20132126. https:\/\/doi.org\/10.1109\/TPAMI.2020.3031625","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2225_CR4","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":"2225_CR5","unstructured":"Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., & Mukhopadhyay, D. (2018). Adversarial attacks and defences: A survey. arXiv preprint arXiv:1810.00069"},{"key":"2225_CR6","doi-asserted-by":"publisher","unstructured":"Chao, H., He, Y., Zhang, J., & Feng, J. (2019). GaitSet: Regarding gait as a set for cross-view gait recognition. in Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp. 8126\u20138133. https:\/\/doi.org\/10.1609\/aaai.v33i01.33018126","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"2225_CR7","doi-asserted-by":"crossref","unstructured":"Cui, Y., & Kang, Y. (2023). Multi-modal gait recognition via effective spatial-temporal feature fusion. in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 17949\u201317957.","DOI":"10.1109\/CVPR52729.2023.01721"},{"key":"2225_CR8","doi-asserted-by":"crossref","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.","DOI":"10.1109\/CVPR.2018.00957"},{"key":"2225_CR9","unstructured":"Fan, C., Hou, S., Huang, Y., & Yu, S. (2023). Exploring deep models for practical gait recognition. arXiv preprint arXiv:2303.03301"},{"key":"2225_CR10","doi-asserted-by":"crossref","unstructured":"Fan, C., Liang, J., Shen, C., Hou, S., Huang, Y., & Yu, S. (2023). OpenGait: Revisiting gait recognition towards better practicality. in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 9707\u20139716.","DOI":"10.1109\/CVPR52729.2023.00936"},{"key":"2225_CR11","doi-asserted-by":"publisher","unstructured":"Fan, C., Ma, J., Jin, D., Shen, C., & Yu, S. (2024). SkeletonGait: Gait recognition using skeleton maps. in Proceedings of the AAAI conference on artificial intelligence, vol. 38, pp. 1662\u20131669. https:\/\/doi.org\/10.1609\/aaai.v38i2.27933","DOI":"10.1609\/aaai.v38i2.27933"},{"key":"2225_CR12","doi-asserted-by":"crossref","unstructured":"Fan, C., Peng, Y., Cao, C., Liu, X., Hou, S., Chi, J., Huang, Y., Li, Q., & He, Z. (2020). GaitPart: Temporal part-based model for gait recognition. in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 14225\u201314233.","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"2225_CR13","doi-asserted-by":"publisher","unstructured":"Goldblum, M., Fowl, L., Feizi, S., & Goldstein, T. (2020). Adversarially robust distillation. in Proceedings of the AAAI conference on artificial intelligence, vol. 34, pp. 3996\u20134003. https:\/\/doi.org\/10.1609\/aaai.v34i04.5816","DOI":"10.1609\/aaai.v34i04.5816"},{"key":"2225_CR14","unstructured":"Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. in ICLR."},{"issue":"2","key":"2225_CR15","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Han","year":"2005","unstructured":"Han, J., & Bhanu, B. (2005). Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2), 316\u2013322. https:\/\/doi.org\/10.1109\/TPAMI.2006.38","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2225_CR16","doi-asserted-by":"crossref","unstructured":"Hendrik\u00a0Metzen, J., Chaithanya\u00a0Kumar, M., Brox, T., & Fischer, V. (2017). Universal adversarial perturbations against semantic image segmentation. in Proceedings of the IEEE international conference on computer vision, pp. 2755\u20132764.","DOI":"10.1109\/ICCV.2017.300"},{"key":"2225_CR17","doi-asserted-by":"publisher","first-page":"109028","DOI":"10.1016\/j.patcog.2022.109028","volume":"133","author":"Z He","year":"2023","unstructured":"He, Z., Wang, W., Dong, J., & Tan, T. (2023). Temporal sparse adversarial attack on sequence-based gait recognition. Pattern Recognition, 133, 109028. https:\/\/doi.org\/10.1016\/j.patcog.2022.109028","journal-title":"Pattern Recognition"},{"key":"2225_CR18","doi-asserted-by":"publisher","unstructured":"Hou, S., Cao, C., Liu, X., & Huang, Y. (2020). Gait lateral network: Learning discriminative and compact representations for gait recognition. in European conference on computer vision. https:\/\/doi.org\/10.1007\/978-3-030-58545-7_22","DOI":"10.1007\/978-3-030-58545-7_22"},{"key":"2225_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/TBIOM.2022.3216857","author":"S Hou","year":"2022","unstructured":"Hou, S., Fan, C., Cao, C., Liu, X., & Huang, Y. (2022). A comprehensive study on the evaluation of silhouette-based gait recognition. IEEE Transactions on Biometrics, Behavior, and Identity Science. https:\/\/doi.org\/10.1109\/TBIOM.2022.3216857","journal-title":"IEEE Transactions on Biometrics, Behavior, and Identity Science"},{"key":"2225_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TBIOM.2021.3074963","author":"S Hou","year":"2021","unstructured":"Hou, S., Liu, X., Cao, C., & Huang, Y. (2021). Set residual network for silhouette-based gait recognition. IEEE Transactions on Biometrics, Behavior, and Identity Science. https:\/\/doi.org\/10.1109\/TBIOM.2021.3074963","journal-title":"IEEE Transactions on Biometrics, Behavior, and Identity Science"},{"key":"2225_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3154723","author":"S Hou","year":"2022","unstructured":"Hou, S., Liu, X., Cao, C., & Huang, Y. (2022). Gait quality aware network: Toward the interpretability of silhouette-based gait recognition. IEEE Transactions on Neural Networks and Learning Systems. https:\/\/doi.org\/10.1109\/TNNLS.2022.3154723","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2225_CR22","unstructured":"Huang, S., Papernot, N., Goodfellow, I., Duan, Y., & Abbeel, P. (2017). Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284"},{"key":"2225_CR23","doi-asserted-by":"crossref","unstructured":"Huang, Z., Xue, D., Shen, X., Tian, X., Li, H., Huang, J., & Hua, X. -S. (2021). 3d local convolutional neural networks for gait recognition. in Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14920\u201314929.","DOI":"10.1109\/ICCV48922.2021.01465"},{"key":"2225_CR24","doi-asserted-by":"crossref","unstructured":"Huang, X., Zhu, D., Wang, H., Wang, X., Yang, B., He, B., Liu, W., & Feng, B. (2021). Context-sensitive temporal feature learning for gait recognition. in Proceedings of the IEEE\/CVF international conference on computer vision, pp. 12909\u201312918.","DOI":"10.1109\/ICCV48922.2021.01267"},{"key":"2225_CR25","doi-asserted-by":"publisher","unstructured":"Jia, M., Yang, H., Huang, D., & Wang, Y. (2019). Attacking gait recognition systems via silhouette guided GANs. in Proceedings of the 27th ACM international conference on multimedia, pp. 638\u2013646. https:\/\/doi.org\/10.1145\/3343031.3351018","DOI":"10.1145\/3343031.3351018"},{"key":"2225_CR26","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. in ICLR."},{"key":"2225_CR27","doi-asserted-by":"publisher","unstructured":"Kos, J., Fischer, I., & Song, D. (2018). Adversarial examples for generative models. in IEEE security and privacy workshops, pp. 36\u201342. https:\/\/doi.org\/10.1109\/SPW.2018.00014","DOI":"10.1109\/SPW.2018.00014"},{"key":"2225_CR28","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I. J., & Bengio, S. (2016). Adversarial examples in the physical world. in Artificial intelligence safety and security, pp. 99\u2013112.","DOI":"10.1201\/9781351251389-8"},{"key":"2225_CR29","doi-asserted-by":"crossref","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y., Yu, S., & Ren, M. (2020). End-to-end model-based gait recognition. in Proceedings of the Asian conference on computer vision.","DOI":"10.1007\/978-3-030-69535-4_1"},{"key":"2225_CR30","doi-asserted-by":"publisher","unstructured":"Li, S., Zhu, S., Paul, S., Roy-Chowdhury, A., Song, C., Krishnamurthy, S., Swami, A., & Chan, K. S. (2020). Connecting the dots: Detecting adversarial perturbations using context inconsistency. in Computer vision\u2013ECCV 2020: 16th European conference, pp. 396\u2013413. https:\/\/doi.org\/10.1007\/978-3-030-58592-1_24","DOI":"10.1007\/978-3-030-58592-1_24"},{"key":"2225_CR31","doi-asserted-by":"publisher","unstructured":"Liang, J., Fan, C., Hou, S., Shen, C., Huang, Y., & Yu, S. (2022). GaitEdge: Beyond plain end-to-end gait recognition for better practicality. in European conference on computer vision. https:\/\/doi.org\/10.1007\/978-3-031-20065-6_22","DOI":"10.1007\/978-3-031-20065-6_22"},{"key":"2225_CR32","doi-asserted-by":"publisher","first-page":"107069","DOI":"10.1016\/j.patcog.2019.107069","volume":"98","author":"R Liao","year":"2020","unstructured":"Liao, R., Yu, S., An, W., & Huang, Y. (2020). A model-based gait recognition method with body pose and human prior knowledge. Pattern Recognition, 98, 107069. https:\/\/doi.org\/10.1016\/j.patcog.2019.107069","journal-title":"Pattern Recognition"},{"key":"2225_CR33","doi-asserted-by":"crossref","unstructured":"Lin, Y. -C., Hong, Z. -W., Liao, Y. -H., Shih, M. -L., Liu, M. -Y., & Sun, M. (2017). Tactics of adversarial attack on deep reinforcement learning agents. arXiv preprint arXiv:1703.06748","DOI":"10.24963\/ijcai.2017\/525"},{"key":"2225_CR34","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhang, S., & Yu, X. (2021) Gait recognition via effective global-local feature representation and local temporal aggregation. in Proceedings of the IEEE\/CVF international conference on computer vision, pp. 14648\u201314656.","DOI":"10.1109\/ICCV48922.2021.01438"},{"key":"2225_CR35","doi-asserted-by":"publisher","unstructured":"Liu, Z., Zhao, Z., & Larson, M. (2019). Who\u2019s afraid of adversarial queries? The impact of image modifications on content-based image retrieval. in Proceedings of the 2019 on international conference on multimedia retrieval, pp. 306\u2013314. https:\/\/doi.org\/10.1145\/3323873.3325052","DOI":"10.1145\/3323873.3325052"},{"key":"2225_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3154609","author":"N Li","year":"2022","unstructured":"Li, N., & Zhao, X. (2022). A strong and robust skeleton-based gait recognition method with gait periodicity priors. IEEE Transactions on Multimedia. https:\/\/doi.org\/10.1109\/TMM.2022.3154609","journal-title":"IEEE Transactions on Multimedia"},{"issue":"6","key":"2225_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2015). SMPL: A skinned multi-person linear model. ACM Transactions on Graphics, 34(6), 1\u201316. https:\/\/doi.org\/10.1145\/2816795.2818013","journal-title":"ACM Transactions on Graphics"},{"key":"2225_CR38","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. in ICLR."},{"key":"2225_CR39","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.isatra.2022.11.016","volume":"132","author":"M Maqsood","year":"2023","unstructured":"Maqsood, M., Yasmin, S., Gillani, S., Aadil, F., Mehmood, I., Rho, S., & Yeo, S.-S. (2023). An autonomous decision-making framework for gait recognition systems against adversarial attack using reinforcement learning. ISA Transactions, 132, 80\u201393. https:\/\/doi.org\/10.1016\/j.isatra.2022.11.016","journal-title":"ISA Transactions"},{"key":"2225_CR40","doi-asserted-by":"crossref","unstructured":"Narodytska, N., & Kasiviswanathan, S. P. (2017). Simple black-box adversarial attacks on deep neural networks. in CVPR workshops, vol. 2, p. 2.","DOI":"10.1109\/CVPRW.2017.172"},{"key":"2225_CR41","doi-asserted-by":"publisher","unstructured":"Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a defense to adversarial perturbations against deep neural networks. in 2016 IEEE symposium on security and privacy (SP), pp. 582\u2013597. https:\/\/doi.org\/10.1109\/SP.2016.41","DOI":"10.1109\/SP.2016.41"},{"key":"2225_CR42","unstructured":"Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017). PointNet: Deep learning on point sets for 3d classification and segmentation. in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 652\u2013660."},{"key":"2225_CR43","unstructured":"Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017). PointNet++: Deep hierarchical feature learning on point sets in a metric space. in NeurIPS, vol. 30."},{"key":"2225_CR44","doi-asserted-by":"crossref","unstructured":"Rony, J., Hafemann, L. G., Oliveira, L. S., Ayed, I. B., Sabourin, R., & Granger, E. (2019). Decoupling direction and norm for efficient gradient-based l2 adversarial attacks and defenses. in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 4322\u20134330.","DOI":"10.1109\/CVPR.2019.00445"},{"key":"2225_CR45","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747"},{"key":"2225_CR46","unstructured":"Sepas-Moghaddam, A., & Etemad, A. (2021). Deep gait recognition: A survey. arXiv preprint arXiv:2102.09546"},{"key":"2225_CR47","doi-asserted-by":"crossref","unstructured":"Shen, C., Fan, C., Wu, W., Wang, R., Huang, G. Q., & Yu, S. (2023). LidarGait: Benchmarking 3d gait recognition with point clouds. in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1054\u20131063.","DOI":"10.1109\/CVPR52729.2023.00108"},{"key":"2225_CR48","unstructured":"Shen, C., Yu, S., Wang, J., Huang, G. Q., & Wang, L. (2022). A comprehensive survey on deep gait recognition: Algorithms, datasets and challenges. arXiv preprint arXiv:2206.13732"},{"key":"2225_CR49","doi-asserted-by":"publisher","unstructured":"Soll, M., Hinz, T., Magg, S., & Wermter, S. (2019). Evaluating defensive distillation for defending text processing neural networks against adversarial examples. in International conference on artificial neural networks, pp. 685\u2013696. https:\/\/doi.org\/10.1007\/978-3-030-30508-6_54","DOI":"10.1007\/978-3-030-30508-6_54"},{"key":"2225_CR50","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199"},{"key":"2225_CR51","unstructured":"Tabacof, P., Tavares, J., & Valle, E. (2016). Adversarial images for variational autoencoders. arXiv preprint arXiv:1612.00155"},{"issue":"1","key":"2225_CR52","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s41074-018-0039-6","volume":"10","author":"N Takemura","year":"2018","unstructured":"Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., & Yagi, Y. (2018). Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Transactions on Computer Vision and Applications, 10(1), 4. https:\/\/doi.org\/10.1186\/s41074-018-0039-6","journal-title":"IPSJ Transactions on Computer Vision and Applications"},{"key":"2225_CR53","doi-asserted-by":"crossref","unstructured":"Teepe, T., Gilg, J., Herzog, F., H\u00f6rmann, S., & Rigoll, G. (2022). Towards a deeper understanding of skeleton-based gait recognition. in CVPR workshop, pp. 1569\u20131577.","DOI":"10.1109\/CVPRW56347.2022.00163"},{"key":"2225_CR54","doi-asserted-by":"publisher","unstructured":"Teepe, T., Khan, A., Gilg, J., Herzog, F., H\u00f6rmann, S., & Rigoll, G. (2021). GaitGraph: Graph convolutional network for skeleton-based gait recognition. in 2021 IEEE international conference on image processing, pp. 2314\u20132318. https:\/\/doi.org\/10.1109\/ICIP42928.2021.9506717","DOI":"10.1109\/ICIP42928.2021.9506717"},{"key":"2225_CR55","doi-asserted-by":"crossref","unstructured":"Tolias, G., Radenovic, F., & Chum, O. (2019). Targeted mismatch adversarial attack: Query with a flower to retrieve the tower. in Proceedings of the IEEE\/CVF international conference on computer vision, pp. 5037\u20135046.","DOI":"10.1109\/ICCV.2019.00514"},{"key":"2225_CR56","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s11263-010-0362-6","volume":"91","author":"I Venkat","year":"2011","unstructured":"Venkat, I., & De Wilde, P. (2011). Robust gait recognition by learning and exploiting sub-gait characteristics. International Journal of Computer Vision, 91, 7\u201323. https:\/\/doi.org\/10.1007\/s11263-010-0362-6","journal-title":"International Journal of Computer Vision"},{"key":"2225_CR57","doi-asserted-by":"crossref","unstructured":"Wang, Y., Du, B., Shen, Y., Wu, K., Zhao, G., Sun, J., & Wen, H. (2019). EV-Gait: Event-based robust gait recognition using dynamic vision sensors. in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 6358\u20136367.","DOI":"10.1109\/CVPR.2019.00652"},{"key":"2225_CR58","doi-asserted-by":"crossref","unstructured":"Wang, M., Guo, X., Lin, B., Yang, T., Zhu, Z., Li, L., Zhang, S., & Yu, X. (2023). Dygait: Exploiting dynamic representations for high-performance gait recognition. in Proceedings of the IEEE\/CVF international conference on computer vision, pp. 13424\u201313433.","DOI":"10.1109\/ICCV51070.2023.01235"},{"key":"2225_CR59","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., & Yuille, A. (2017). Adversarial examples for semantic segmentation and object detection. in Proceedings of the IEEE international conference on computer vision, pp. 1369\u20131378.","DOI":"10.1109\/ICCV.2017.153"},{"key":"2225_CR60","doi-asserted-by":"crossref","unstructured":"Yin, M., Li, S., Cai, Z., Song, C., Asif, M. S., Roy-Chowdhury, A. K., & Krishnamurthy, S. V. (2021). Exploiting multi-object relationships for detecting adversarial attacks in complex scenes. in Proceedings of the IEEE\/CVF international conference on computer vision, pp. 7858\u20137867.","DOI":"10.1109\/ICCV48922.2021.00776"},{"key":"2225_CR61","doi-asserted-by":"crossref","unstructured":"Yin, M., Li, S., Song, C., Asif, M. S., Roy-Chowdhury, A. K., & Krishnamurthy, S. V. (2022). ADC: Adversarial attacks against object detection that evade context consistency checks. in Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp. 3278\u20133287.","DOI":"10.1109\/WACV51458.2022.00289"},{"key":"2225_CR62","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1109\/ICPR.2006.67","volume":"4","author":"S Yu","year":"2006","unstructured":"Yu, S., Tan, D., & Tan, T. (2006). A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. ICPR, 4, 441\u2013444. https:\/\/doi.org\/10.1109\/ICPR.2006.67","journal-title":"ICPR"},{"key":"2225_CR63","doi-asserted-by":"crossref","unstructured":"Zheng, T., Chen, C., Yuan, J., Li, B., & Ren, K. (2019). Pointcloud saliency maps. in Proceedings of the IEEE\/CVF international conference on computer vision, pp. 1598\u20131606.","DOI":"10.1109\/ICCV.2019.00168"},{"key":"2225_CR64","doi-asserted-by":"crossref","unstructured":"Zheng, J., Liu, X., Liu, W., He, L., Yan, C., & Mei, T. (2022). Gait recognition in the wild with dense 3d representations and a benchmark. in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 20228\u201320237.","DOI":"10.1109\/CVPR52688.2022.01959"},{"key":"2225_CR65","unstructured":"Zheng, Z., Zheng, L., Hu, Z., & Yang, Y. (2018). Open set adversarial examples. arXiv preprint arXiv:1809.02681"},{"key":"2225_CR66","unstructured":"Zhu, Z., Guo, X., Yang, T., Huang, J., Deng, J., Huang, G., Du, D., Lu, J., & Zhou, J. (2021). Gait recognition in the wild: A benchmark. in Proceedings of the IEEE\/CVF international conference on computer vision, pp. 14789\u201314799."}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02225-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02225-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02225-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T22:05:58Z","timestamp":1743372358000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02225-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":66,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["2225"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02225-1","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"30 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}