{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T05:57:47Z","timestamp":1743055067081,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031306747"},{"type":"electronic","value":"9783031306754"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-30675-4_32","type":"book-chapter","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T10:02:24Z","timestamp":1681466544000},"page":"441-456","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BACH: Black-Box Attacking on\u00a0Deep Cross-Modal Hamming Retrieval Models"],"prefix":"10.1007","author":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianyu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","unstructured":"Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2006), pp. 459\u2013468. IEEE (2006)","DOI":"10.1109\/FOCS.2006.49"},{"key":"32_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-030-01246-5_13","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Cao","year":"2018","unstructured":"Cao, Y., Liu, B., Long, M., Wang, J.: Cross-modal hamming hashing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 207\u2013223. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_13"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Y., Long, M., Wang, J., Yang, Q., Yu, P.S.: Deep visual-semantic hashing for cross-modal retrieval. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1445\u20131454 (2016)","DOI":"10.1145\/2939672.2939812"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 39\u201357. IEEE (2017)","DOI":"10.1109\/SP.2017.49"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Ding, G., Guo, Y., Zhou, J.: Collective matrix factorization hashing for multimodal data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2075\u20132082 (2014)","DOI":"10.1109\/CVPR.2014.267"},{"issue":"11","key":"32_CR6","doi-asserted-by":"publisher","first-page":"5427","DOI":"10.1109\/TIP.2016.2607421","volume":"25","author":"G Ding","year":"2016","unstructured":"Ding, G., Guo, Y., Zhou, J., Gao, Y.: Large-scale cross-modality search via collective matrix factorization hashing. IEEE Trans. Image Process. 25(11), 5427\u20135440 (2016)","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"32_CR7","doi-asserted-by":"publisher","first-page":"2916","DOI":"10.1109\/TPAMI.2012.193","volume":"35","author":"Y Gong","year":"2012","unstructured":"Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916\u20132929 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"32_CR8","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Gu, W., Gu, X., Gu, J., Li, B., Xiong, Z., Wang, W.: Adversary guided asymmetric hashing for cross-modal retrieval. In: Proceedings of the 2019 on International Conference on Multimedia Retrieval, pp. 159\u2013167 (2019)","DOI":"10.1145\/3323873.3325045"},{"issue":"8","key":"32_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"32_CR11","unstructured":"Ilyas, A., Engstrom, L., Madry, A.: Prior convictions: black-box adversarial attacks with bandits and priors. In: International Conference on Learning Representations (2018)"},{"key":"32_CR12","unstructured":"Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A.: Adversarial examples are not bugs, they are features. In; Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 604\u2013613 (1998)","DOI":"10.1145\/276698.276876"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, Q.Y., Li, W.J.: Deep cross-modal hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3232\u20133240 (2017)","DOI":"10.1109\/CVPR.2017.348"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Jiang, Q.Y., Li, W.J.: Asymmetric deep supervised hashing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11814"},{"key":"32_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"7553","key":"32_CR17","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Li, C., Deng, C., Li, N., Liu, W., Gao, X., Tao, D.: Self-supervised adversarial hashing networks for cross-modal retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4242\u20134251 (2018)","DOI":"10.1109\/CVPR.2018.00446"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Li, C., Gao, S., Deng, C., Liu, W., Huang, H.: Adversarial attack on deep cross-modal hamming retrieval. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2218\u20132227 (2021)","DOI":"10.1109\/ICCV48922.2021.00222"},{"key":"32_CR20","unstructured":"Li, C., Gao, S., Deng, C., Xie, D., Liu, W.: Cross-modal learning with adversarial samples. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"32_CR21","unstructured":"Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., van Gemert, J.: Deep unsupervised image hashing by maximizing bit entropy. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 2002\u20132010 (2021)","DOI":"10.1609\/aaai.v35i3.16296"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Lin, Z., Ding, G., Hu, M., Wang, J.: Semantics-preserving hashing for cross-view retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3864\u20133872 (2015)","DOI":"10.1109\/CVPR.2015.7299011"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Liu, H., Wang, R., Shan, S., Chen, X.: Deep supervised hashing for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2064\u20132072 (2016)","DOI":"10.1109\/CVPR.2016.227"},{"issue":"1","key":"32_CR25","first-page":"33","volume":"1","author":"J Liu","year":"2010","unstructured":"Liu, J., Xu, C., Lu, H.: Cross-media retrieval: state-of-the-art and open issues. Int. J. Multimedia Intell. Secur. 1(1), 33\u201352 (2010)","journal-title":"Int. J. Multimedia Intell. Secur."},{"issue":"10","key":"32_CR26","doi-asserted-by":"publisher","first-page":"4514","DOI":"10.1109\/TIP.2016.2593344","volume":"25","author":"X Liu","year":"2016","unstructured":"Liu, X., Huang, L., Deng, C., Lang, B., Tao, D.: Query-adaptive hash code ranking for large-scale multi-view visual search. IEEE Trans. Image Process. 25(10), 4514\u20134524 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Long, M., Cao, Y., Wang, J., Yu, P.S.: Composite correlation quantization for efficient multimodal retrieval. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 579\u2013588 (2016)","DOI":"10.1145\/2911451.2911493"},{"key":"32_CR28","unstructured":"Nakkiran, P.: Adversarial robustness may be at odds with simplicity. arXiv preprint arXiv:1901.00532 (2019)"},{"issue":"2","key":"32_CR29","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s10208-015-9296-2","volume":"17","author":"Y Nesterov","year":"2017","unstructured":"Nesterov, Y., Spokoiny, V.: Random gradient-free minimization of convex functions. Found. Comput. Math. 17(2), 527\u2013566 (2017)","journal-title":"Found. Comput. Math."},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Shen, F., Shen, C., Liu, W., Tao Shen, H.: Supervised discrete hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 37\u201345 (2015)","DOI":"10.1109\/CVPR.2015.7298598"},{"key":"32_CR31","doi-asserted-by":"crossref","unstructured":"Song, J., Yang, Y., Yang, Y., Huang, Z., Shen, H.T.: Inter-media hashing for large-scale retrieval from heterogeneous data sources. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 785\u2013796 (2013)","DOI":"10.1145\/2463676.2465274"},{"key":"32_CR32","doi-asserted-by":"crossref","unstructured":"Su, S., Zhong, Z., Zhang, C.: Deep joint-semantics reconstructing hashing for large-scale unsupervised cross-modal retrieval. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3027\u20133035 (2019)","DOI":"10.1109\/ICCV.2019.00312"},{"key":"32_CR33","unstructured":"Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"32_CR34","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"32_CR35","doi-asserted-by":"crossref","unstructured":"Wu, D., Dai, Q., Liu, J., Li, B., Wang, W.: Deep incremental hashing network for efficient image retrieval. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9069\u20139077 (2019)","DOI":"10.1109\/CVPR.2019.00928"},{"key":"32_CR36","unstructured":"Wu, Y., et al.: Google\u2019s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)"},{"key":"32_CR37","unstructured":"Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013)"},{"key":"32_CR38","doi-asserted-by":"crossref","unstructured":"Yang, E., Deng, C., Liu, W., Liu, X., Tao, D., Gao, X.: Pairwise relationship guided deep hashing for cross-modal retrieval. In: proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10719"},{"key":"32_CR39","doi-asserted-by":"crossref","unstructured":"Yuan, L., et al.: Central similarity quantization for efficient image and video retrieval. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3083\u20133092 (2020)","DOI":"10.1109\/CVPR42600.2020.00315"},{"issue":"9","key":"32_CR40","doi-asserted-by":"publisher","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","volume":"30","author":"X Yuan","year":"2019","unstructured":"Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defenses for deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30(9), 2805\u20132824 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"32_CR41","doi-asserted-by":"crossref","unstructured":"Zhai, X., Peng, Y., Xiao, J.: Heterogeneous metric learning with joint graph regularization for cross-media retrieval. In: Twenty-Seventh AAAI Conference on Artificial Intelligence (2013)","DOI":"10.1609\/aaai.v27i1.8464"},{"key":"32_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, D., Li, W.J.: Large-scale supervised multimodal hashing with semantic correlation maximization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8995"},{"key":"32_CR43","doi-asserted-by":"crossref","unstructured":"Zhou, J., Ding, G., Guo, Y.: Latent semantic sparse hashing for cross-modal similarity search. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 415\u2013424 (2014)","DOI":"10.1145\/2600428.2609610"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30675-4_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:10:58Z","timestamp":1710245458000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30675-4_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306747","9783031306754"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30675-4_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"15 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","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":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"652","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":"125","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":"66","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":"19% - 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":"7.3","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)"}}]}}