{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:50:00Z","timestamp":1776275400048,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819708079","type":"print"},{"value":"9789819708086","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-0808-6_10","type":"book-chapter","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T16:02:20Z","timestamp":1708963340000},"page":"163-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Black-Box Graph Backdoor Defense"],"prefix":"10.1007","author":[{"given":"Xiao","family":"Yang","sequence":"first","affiliation":[]},{"given":"Gaolei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaoyi","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Chaofeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jianhua","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,27]]},"reference":[{"issue":"8","key":"10_CR1","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics9081295","volume":"9","author":"M Ahmed","year":"2020","unstructured":"Ahmed, M., Seraj, R., Islam, S.M.S.: The k-means algorithm: a comprehensive survey and performance evaluation. Electronics 9(8), 1295 (2020)","journal-title":"Electronics"},{"key":"10_CR2","doi-asserted-by":"publisher","unstructured":"Chen, G., Wu, J., Yang, W., Bashir, A.K., Li, G., Hammoudeh, M.: Leveraging graph convolutional-lstm for energy-efficient caching in blockchain-based green iot. IEEE Trans. Green Commun. Netw. 5(3), 1154\u20131164 (2021). https:\/\/doi.org\/10.1109\/TGCN.2021.3069395","DOI":"10.1109\/TGCN.2021.3069395"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chen, H., Fu, C., Zhao, J., Koushanfar, F.: Deepinspect: a black-box trojan detection and mitigation framework for deep neural networks. In: IJCAI. vol. 2, p. 8 (2019)","DOI":"10.24963\/ijcai.2019\/647"},{"key":"10_CR4","unstructured":"Chen, J., Xiong, H., Zheng, H., Zhang, J., Jiang, G., Liu, Y.: Dyn-backdoor: backdoor attack on dynamic link prediction. arXiv preprint arXiv:2110.03875 (2021)"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: Neighboring backdoor attacks on graph convolutional network. arXiv preprint arXiv:2201.06202 (2022)","DOI":"10.2139\/ssrn.4406116"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ye, Z., Zhao, H., Wang, Y., et al.: Feature-based graph backdoor attack in the node classification task. Int. J. Intell. Syst. 2023 (2023)","DOI":"10.1155\/2023\/5418398"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Dai, E., Lin, M., Zhang, X., Wang, S.: Unnoticeable backdoor attacks on graph neural networks. arXiv preprint arXiv:2303.01263 (2023)","DOI":"10.1145\/3543507.3583392"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Y., et al.: Black-box detection of backdoor attacks with limited information and data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16482\u201316491 (2021)","DOI":"10.1109\/ICCV48922.2021.01617"},{"key":"10_CR9","unstructured":"Guo, J., Li, A., Liu, C.: Aeva: Black-box backdoor detection using adversarial extreme value analysis. arXiv preprint arXiv:2110.14880 (2021)"},{"key":"10_CR10","unstructured":"Guo, J., Li, Y., Chen, X., Guo, H., Sun, L., Liu, C.: Scale-up: an efficient black-box input-level backdoor detection via analyzing scaled prediction consistency. arXiv preprint arXiv:2302.03251 (2023)"},{"key":"10_CR11","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017(December), pp. 4\u20139, 2017. Long Beach, CA, USA, pp. 1024\u20131034 (2017), https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html"},{"key":"10_CR12","unstructured":"Jiang, B., Li, Z.: Defending against backdoor attack on graph nerual network by explainability. arXiv preprint arXiv:2209.02902 (2022)"},{"key":"10_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings. OpenReview.net (2017). https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"10_CR14","unstructured":"Li, Y., Wu, B., Jiang, Y., Li, Z., Xia, S.: Backdoor learning: a survey. CoRR abs\/2007.08745 (2020). https:\/\/arxiv.org\/abs\/2007.08745"},{"key":"10_CR15","doi-asserted-by":"publisher","unstructured":"Li, Y., Li, Y., Wu, B., Li, L., He, R., Lyu, S.: Invisible backdoor attack with sample-specific triggers. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10\u201317, 2021, pp. 16443\u201316452. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01615","DOI":"10.1109\/ICCV48922.2021.01615"},{"key":"10_CR16","doi-asserted-by":"publisher","unstructured":"Liu, Y., et al.: Backdoor defense with machine unlearning. In: IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, London, United Kingdom, May 2\u20135, 2022, pp. 280\u2013289. IEEE (2022). https:\/\/doi.org\/10.1109\/INFOCOM48880.2022.9796974","DOI":"10.1109\/INFOCOM48880.2022.9796974"},{"key":"10_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1007\/978-3-030-58607-2_11","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Ma, X., Bailey, J., Lu, F.: Reflection backdoor: a natural backdoor attack on deep neural networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12355, pp. 182\u2013199. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58607-2_11"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Reynolds, D.A., et al.: Gaussian mixture models. Encycl. Biometrics 741(659\u2013663) (2009)","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.jprocont.2018.12.010","volume":"73","author":"N Sammaknejad","year":"2019","unstructured":"Sammaknejad, N., Zhao, Y., Huang, B.: A review of the expectation maximization algorithm in data-driven process identification. J. Process Control 73, 123\u2013136 (2019)","journal-title":"J. Process Control"},{"issue":"20","key":"10_CR20","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. stat 1050(20), 10\u201348550 (2017)","journal-title":"Graph attention networks. stat"},{"key":"10_CR21","unstructured":"Wang, X., Zhang, M.: How powerful are spectral graph neural networks. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesv\u00e1ri, C., Niu, G., Sabato, S. (eds.) International Conference on Machine Learning, ICML 2022, 17\u201323 July 2022, Baltimore, Maryland, USA. Proceedings of Machine Learning Research, vol. 162, pp. 23341\u201323362. PMLR (2022). https:\/\/proceedings.mlr.press\/v162\/wang22am.html"},{"key":"10_CR22","first-page":"11973","volume":"33","author":"CH Weng","year":"2020","unstructured":"Weng, C.H., Lee, Y.T., Wu, S.H.B.: On the trade-off between adversarial and backdoor robustness. Adv. Neural. Inf. Process. Syst. 33, 11973\u201311983 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"10_CR23","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10_CR24","unstructured":"Xi, Z., Pang, R., Ji, S., Wang, T.: Graph backdoor. In: USENIX Security Symposium, pp. 1523\u20131540 (2021)"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Xu, J., Wang, R., Liang, K., Picek, S.: More is better (mostly): On the backdoor attacks in federated graph neural networks. arXiv preprint arXiv:2202.03195 (2022)","DOI":"10.1145\/3564625.3567999"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Xu, J., Xue, M., Picek, S.: Explainability-based backdoor attacks against graph neural networks. In: Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning, pp. 31\u201336 (2021)","DOI":"10.1145\/3468218.3469046"},{"key":"10_CR27","doi-asserted-by":"publisher","unstructured":"Yan, Z., et al.: Dehib: Deep hidden backdoor attack on semi-supervised learning via adversarial perturbation. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2\u20139, 2021, pp. 10585\u201310593. AAAI Press (2021). https:\/\/doi.org\/10.1609\/aaai.v35i12.17266","DOI":"10.1609\/aaai.v35i12.17266"},{"key":"10_CR28","doi-asserted-by":"publisher","unstructured":"Yan, Z., Li, S., Zhao, R., Tian, Y., Zhao, Y.: DHBE: data-free holistic backdoor erasing in deep neural networks via restricted adversarial distillation. In: Liu, J.K., Xiang, Y., Nepal, S., Tsudik, G. (eds.) Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security, ASIA CCS 2023, Melbourne, VIC, Australia, July 10\u201314, 2023, pp. 731\u2013745. ACM (2023). https:\/\/doi.org\/10.1145\/3579856.3582822","DOI":"10.1145\/3579856.3582822"},{"key":"10_CR29","doi-asserted-by":"publisher","unstructured":"Yan, Z., Wu, J., Li, G., Li, S., Guizani, M.: Deep neural backdoor in semi-supervised learning: threats and countermeasures. IEEE Trans. Inf. Forensics Secur. 16, 4827\u20134842 (2021). https:\/\/doi.org\/10.1109\/TIFS.2021.3116431","DOI":"10.1109\/TIFS.2021.3116431"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: Transferable graph backdoor attack. In: Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses, pp. 321\u2013332 (2022)","DOI":"10.1145\/3545948.3545976"},{"key":"10_CR31","doi-asserted-by":"publisher","unstructured":"Yao, Y., Li, H., Zheng, H., Zhao, B.Y.: Latent backdoor attacks on deep neural networks. In: Cavallaro, L., Kinder, J., Wang, X., Katz, J. (eds.) Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, CCS 2019, London, UK, November 11\u201315, 2019, pp. 2041\u20132055. ACM (2019). https:\/\/doi.org\/10.1145\/3319535.3354209","DOI":"10.1145\/3319535.3354209"},{"key":"10_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2\u20137, 2018, pp. 4438\u20134445. AAAI Press (2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11782","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"10_CR33","unstructured":"Zhang, X., Chen, H., Huang, K., Koushanfar, F.: An adaptive black-box backdoor detection method for deep neural networks (2022)"},{"key":"10_CR34","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Jia, J., Wang, B., Gong, N.Z.: Backdoor attacks to graph neural networks. In: Proceedings of the 26th ACM Symposium on Access Control Models and Technologies, pp. 15\u201326. SACMAT \u201921, Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3450569.3463560","DOI":"10.1145\/3450569.3463560"},{"key":"10_CR35","unstructured":"Zheng, H., Xiong, H., Chen, J., Ma, H., Huang, G.: Motif-backdoor: rethinking the backdoor attack on graph neural networks via motifs. arXiv preprint arXiv:2210.13710 (2022)"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-0808-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T16:04:01Z","timestamp":1708963441000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-0808-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819708079","9789819708086"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-0808-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tjutanklab.com\/ica3pp2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Online submission system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"439","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":"145","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":"33% - 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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}