{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:15:59Z","timestamp":1771265759132,"version":"3.50.1"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031197710","type":"print"},{"value":"9783031197727","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19772-7_42","type":"book-chapter","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T22:09:58Z","timestamp":1666908598000},"page":"725-742","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Boosting Transferability of\u00a0Targeted Adversarial Examples via\u00a0Hierarchical Generative Networks"],"prefix":"10.1007","author":[{"given":"Xiao","family":"Yang","sequence":"first","affiliation":[]},{"given":"Yinpeng","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Tianyu","family":"Pang","sequence":"additional","affiliation":[]},{"given":"Hang","family":"Su","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"42_CR1","unstructured":"Berthelot, D., Schumm, T., Metz, L.: Began: Boundary equilibrium generative adversarial networks. arXiv preprint arXiv:1703.10717 (2017)"},{"key":"42_CR2","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)"},{"key":"42_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/978-3-319-97909-0_46","volume-title":"Biometric Recognition","author":"S Chen","year":"2018","unstructured":"Chen, S., Liu, Y., Gao, X., Han, Z.: MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices. In: Zhou, J., et al. (eds.) CCBR 2018. LNCS, vol. 10996, pp. 428\u2013438. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-97909-0_46"},{"key":"42_CR4","unstructured":"Demontis, A., et al.: Why do adversarial attacks transfer? Explaining transferability of evasion and poisoning attacks. In: 28th USENIX Security Symposium (USENIX Security 2019), pp. 321\u2013338 (2019)"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference On Computer Vision and Pattern Recognition. pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"42_CR7","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Benchmarking adversarial robustness. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00040"},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., Li, J.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00957"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Dong, Y., Pang, T., Su, H., Zhu, J.: Evading defenses to transferable adversarial examples by translation-invariant attacks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00444"},{"key":"42_CR10","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1625\u20131634 (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"42_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/978-3-030-58604-1_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"L Gao","year":"2020","unstructured":"Gao, L., Zhang, Q., Song, J., Liu, X., Shen, H.T.: Patch-Wise attack for fooling deep neural network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12373, pp. 307\u2013322. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58604-1_19"},{"key":"42_CR12","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, London (2016). http:\/\/www.deeplearningbook.org"},{"key":"42_CR13","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"42_CR14","doi-asserted-by":"crossref","unstructured":"Han, J., et al.: Once a man: towards multi-target attack via learning multi-target adversarial network once. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5158\u20135167 (2019)","DOI":"10.1109\/ICCV.2019.00526"},{"key":"42_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"42_CR16","unstructured":"Hendrycks, D., Carlini, N., Schulman, J., Steinhardt, J.: Unsolved problems in ml safety. arXiv preprint arXiv:2109.13916 (2021)"},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"42_CR18","unstructured":"Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. Technical report (2007)"},{"key":"42_CR19","doi-asserted-by":"crossref","unstructured":"Huang, Q., Katsman, I., He, H., Gu, Z., Belongie, S., Lim, S.N.: Enhancing adversarial example transferability with an intermediate level attack. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4733\u20134742 (2019)","DOI":"10.1109\/ICCV.2019.00483"},{"key":"42_CR20","first-page":"20791","volume":"33","author":"N Inkawhich","year":"2020","unstructured":"Inkawhich, N., et al.: Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability. Adv. Neural. Inf. Process. Syst. 33, 20791\u201320801 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"42_CR21","unstructured":"Inkawhich, N., Liang, K.J., Carin, L., Chen, Y.: Transferable perturbations of deep feature distributions. arXiv preprint arXiv:2004.12519 (2020)"},{"key":"42_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"42_CR23","doi-asserted-by":"crossref","unstructured":"Kulesza, A., Taskar, B.: k-DPPS: fixed-size determinantal point processes. In: ICML (2011)","DOI":"10.1561\/9781601986290"},{"key":"42_CR24","doi-asserted-by":"crossref","unstructured":"Kulesza, A., Taskar, B.: Determinantal point processes for machine learning. arXiv preprint arXiv:1207.6083 (2012)","DOI":"10.1561\/9781601986290"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. In: International Conference on Learning Representations (ICLR) Workshops (2017)","DOI":"10.1201\/9781351251389-8"},{"key":"42_CR26","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/978-3-319-94042-7_11","volume-title":"The NIPS \u201917 Competition: Building Intelligent Systems","author":"A Kurakin","year":"2018","unstructured":"Kurakin, A., et al.: Adversarial attacks and defences competition. In: Escalera, S., Weimer, M. (eds.) The NIPS \u201917 Competition: Building Intelligent Systems. TSSCML, pp. 195\u2013231. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-94042-7_11"},{"key":"42_CR27","doi-asserted-by":"crossref","unstructured":"Li, M., Deng, C., Li, T., Yan, J., Gao, X., Huang, H.: Towards transferable targeted attack. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 641\u2013649 (2020)","DOI":"10.1109\/CVPR42600.2020.00072"},{"key":"42_CR28","doi-asserted-by":"crossref","unstructured":"Li, Y., Bai, S., Xie, C., Liao, Z., Shen, X., Yuille, A.L.: Regional homogeneity: towards learning transferable universal adversarial perturbations against defenses. arXiv preprint arXiv:1904.00979 (2019)","DOI":"10.1007\/978-3-030-58621-8_46"},{"key":"42_CR29","unstructured":"Lin, J., Song, C., He, K., Wang, L., Hopcroft, J.E.: Nesterov accelerated gradient and scale invariance for adversarial attacks. In: International Conference on Learning Representations (2019)"},{"key":"42_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T Lin","year":"2014","unstructured":"Lin, T., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"42_CR31","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 212\u2013220 (2017)","DOI":"10.1109\/CVPR.2017.713"},{"key":"42_CR32","unstructured":"Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. In: ICLR (2017)"},{"key":"42_CR33","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":"42_CR34","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.282"},{"key":"42_CR35","unstructured":"Naseer, M.M., Khan, S.H., Khan, M.H., Khan, F.S., Porikli, F.: Cross-domain transferability of adversarial perturbations. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 12905\u201312915 (2019)"},{"key":"42_CR36","doi-asserted-by":"crossref","unstructured":"Naseer, M., Khan, S., Hayat, M., Khan, F.S., Porikli, F.: On generating transferable targeted perturbations. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7708\u20137717 (2021)","DOI":"10.1109\/ICCV48922.2021.00761"},{"key":"42_CR37","unstructured":"Kaggle: NeurIPS (2017). http:\/\/www.kaggle.com\/c\/nips-2017-defense-against-adversarial-attack\/data"},{"key":"42_CR38","doi-asserted-by":"crossref","unstructured":"Poursaeed, O., Katsman, I., Gao, B., Belongie, S.: Generative adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4422\u20134431 (2018)","DOI":"10.1109\/CVPR.2018.00465"},{"key":"42_CR39","doi-asserted-by":"crossref","unstructured":"Reddy Mopuri, K., Krishna Uppala, P., Venkatesh Babu, R.: Ask, acquire, and attack: data-free UAP generation using class impressions. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 19\u201334 (2018)","DOI":"10.1007\/978-3-030-01240-3_2"},{"key":"42_CR40","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"42_CR41","doi-asserted-by":"crossref","unstructured":"Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: Accessorize to a crime: real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 1528\u20131540 (2016)","DOI":"10.1145\/2976749.2978392"},{"key":"42_CR42","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"42_CR43","unstructured":"Song, Y., Shu, R., Kushman, N., Ermon, S.: Constructing unrestricted adversarial examples with generative models. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems (2018)"},{"key":"42_CR44","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"42_CR45","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"42_CR46","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"42_CR47","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. In: International Conference on Learning Representations (ICLR) (2014)"},{"key":"42_CR48","unstructured":"Tram\u00e8r, F., Kurakin, A., Papernot, N., Boneh, D., McDaniel, P.: Ensemble adversarial training: attacks and defenses. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"42_CR49","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265\u20135274 (2018)","DOI":"10.1109\/CVPR.2018.00552"},{"key":"42_CR50","unstructured":"Wu, D., Wang, Y., Xia, S.T., Bailey, J., Ma, X.: Skip connections matter: On the transferability of adversarial examples generated with resnets. arXiv preprint arXiv:2002.05990 (2020)"},{"key":"42_CR51","doi-asserted-by":"crossref","unstructured":"Xie, C., et al.: Improving transferability of adversarial examples with input diversity. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00284"},{"key":"42_CR52","unstructured":"Xu, K., Li, C., Zhu, J., Zhang, B.: Understanding and stabilizing GANs\u2019 training dynamics with control theory. arXiv preprint arXiv:1909.13188 (2019)"},{"key":"42_CR53","unstructured":"Yang, X., Dong, Y., Pang, T., Xiao, Z., Su, H., Zhu, J.: Controllable evaluation and generation of physical adversarial patch on face recognition. arXiv e-prints pp. arXiv-2203 (2022)"},{"key":"42_CR54","doi-asserted-by":"crossref","unstructured":"Yang, X., Dong, Y., Pang, T., Zhu, J., Su, H.: Towards privacy protection by generating adversarial identity masks. arXiv preprint arXiv:2003.06814 (2020)","DOI":"10.1109\/ICCV48922.2021.00387"},{"key":"42_CR55","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-030-58520-4_11","volume-title":"Computer Vision \u2013 ECCV 2020","author":"X Yang","year":"2020","unstructured":"Yang, X., Wei, F., Zhang, H., Zhu, J.: Design and interpretation of universal adversarial patches in face detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 174\u2013191. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_11"},{"key":"42_CR56","unstructured":"Yang, X., Yang, D., Dong, Y., Yu, W., Su, H., Zhu, J.: Delving into the adversarial robustness on face recognition. arXiv preprint arXiv:2007.04118 (2020)"},{"key":"42_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14521\u201314530 (2020)","DOI":"10.1109\/CVPR42600.2020.01453"},{"key":"42_CR58","unstructured":"Zhao, Z., Liu, Z., Larson, M.: On success and simplicity: a second look at transferable targeted attacks. In: Proceedings of 34th Iinternational Conference on Advances in Neural Information Processing Systems (2021)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19772-7_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:59:18Z","timestamp":1710262758000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19772-7_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197710","9783031197727"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19772-7_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}