{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:05:11Z","timestamp":1777655111411,"version":"3.51.4"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585280","type":"print"},{"value":"9783030585297","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58529-7_5","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T09:06:09Z","timestamp":1605171969000},"page":"69-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Robust Tracking Against Adversarial Attacks"],"prefix":"10.1007","author":[{"given":"Shuai","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibing","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaokang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: Fully-convolutional siamese networks for object tracking. In: ECCV Workshop (2016)","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Bhat, G., Danelljan, M., Van Gool, L., Timofte, R.: Learning discriminative model prediction for tracking. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00628"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Atom: accurate tracking by overlap maximization. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00479"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: ECO: efficient convolution operators for tracking. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.733"},{"key":"5_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-319-46454-1_29","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Danelljan","year":"2016","unstructured":"Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 472\u2013488. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_29"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Efficient decision-based black-box adversarial attacks on face recognition. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00790"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning models. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"5_CR8","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)"},{"key":"5_CR9","unstructured":"Guo, C., Rana, M., Cisse, M., Van Der Maaten, L.: Countering adversarial images using input transformations. In: ICLR (2018)"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Han, B., Sim, J., Adam, H.: Branchout: regularization for online ensemble tracking with convolutional neural networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.63"},{"key":"5_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/978-3-319-46448-0_45","volume-title":"Computer Vision \u2013 ECCV 2016","author":"D Held","year":"2016","unstructured":"Held, D., Thrun, S., Savarese, S.: Learning to track at 100 FPS with deep regression networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 749\u2013765. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_45"},{"key":"5_CR12","unstructured":"Ilyas, A., Engstrom, L., Athalye, A., Lin, J.: Black-box adversarial attacks with limited queries and information. arXiv preprint: 1804.08598 (2018)"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Jung, I., Son, J., Baek, M., Han, B.: Real-time mdnet. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01225-0_6"},{"key":"5_CR14","unstructured":"Kristan, M., et al.: The sixth visual object tracking vot2018 challenge results. In: ECCV Workshop (2018)"},{"key":"5_CR15","unstructured":"Kristan, M., et al.: The visual object tracking vot2016 challenge results. In: ECCV Workshop (2016)"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. In: ICLR (2017)","DOI":"10.1201\/9781351251389-8"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: Siamrpn++: evolution of siamese visual tracking with very deep networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00441"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with siamese region proposal network. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00935"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Li, F., Tian, C., Zuo, W., Zhang, L., Yang, M.H.: Learning spatial-temporal regularized correlation filters for visual tracking. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00515"},{"key":"5_CR20","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: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00191"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Lu, X., Ma, C., Ni, B., Yang, X., Reid, I., Yang, M.H.: Deep regression tracking with shrinkage loss. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01264-9_22"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Lu, X., Wang, W., Ma, C., Shen, J., Shao, L., Porikli, F.: See more, know more: unsupervised video object segmentation with co-attention siamese networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00374"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.352"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"5_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/978-3-319-46448-0_27","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Mueller","year":"2016","unstructured":"Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445\u2013461. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_27"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.465"},{"key":"5_CR28","unstructured":"Pu, S., Song, Y., Ma, C., Zhang, H., Yang, M.H.: Deep attentive tracking via reciprocative learning. In: NeurIPS (2018)"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Qi, Y., et al.: Hedged deep tracking. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.466"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. TPAMI (2016)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R.W., Yang, M.H.: CREST: convolutional residual learning for visual tracking. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.279"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Song, Y., et al.: VITAL: visual tracking via adversarial learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00937"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Sun, B., Tsai, N.H., Liu, F., Yu, R., Su, H.: Adversarial defense by stratified convolutional sparse coding. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01171"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Sun, Y., Sun, C., Wang, D., He, Y., Lu, H.: Roi pooled correlation filters for visual tracking. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00593"},{"key":"5_CR35","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: ICLR (2014)"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.: End-to-end representation learning for correlation filter based tracking. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.531"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.357"},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Wang, N., Song, Y., Ma, C., Zhou, W., Liu, W., Li, H.: Unsupervised deep tracking. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00140"},{"key":"5_CR39","doi-asserted-by":"publisher","unstructured":"Wang, N., Zhou, W., Song, Y., Ma, C., Liu, W., Li, H.: Unsupervised deep representation learning for real-time tracking. IJCV (2020). https:\/\/doi.org\/10.1007\/s11263-020-01357-4","DOI":"10.1007\/s11263-020-01357-4"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Wiyatno, R.R., Xu, A.: Physical adversarial textures that fool visual object tracking. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00492"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. TPAMI (2015)","DOI":"10.1109\/TPAMI.2014.2388226"},{"key":"5_CR42","doi-asserted-by":"crossref","unstructured":"Xiao, C., Deng, R., Li, B., Yu, F., Liu, M., Song, D.: Characterizing adversarial examples based on spatial consistency information for semantic segmentation. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01249-6_14"},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.153"},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Xie, C., Wu, Y., Maaten, L.V.d., Yuille, A.L., He, K.: Feature denoising for improving adversarial robustness. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00059"},{"key":"5_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, T., Xu, C., Yang, M.H.: Multi-task correlation particle filter for robust object tracking. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.512"},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Peng, H.: Deeper and wider siamese networks for real-time visual tracking. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00472"},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware siamese networks for visual object tracking. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01240-3_7"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58529-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:29:58Z","timestamp":1731371398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58529-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585280","9783030585297"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58529-7_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"13 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}