乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T15:38:15Z","timestamp":1726241895857},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819984282"},{"type":"electronic","value":"9789819984299"}],"license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"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\/978-981-99-8429-9_38","type":"book-chapter","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T03:02:17Z","timestamp":1703300537000},"page":"474-486","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Adversarial Training with\u00a0Membership Inference Resistance"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7891-0624","authenticated-orcid":false,"given":"Ran","family":"Yan","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3634-3385","authenticated-orcid":false,"given":"Ruiying","family":"Du","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3472-419X","authenticated-orcid":false,"given":"Kun","family":"He","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7212-5297","authenticated-orcid":false,"given":"Jing","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Andor, D., et al.: Globally normalized transition-based neural networks. In: ACL (2016). https:\/\/doi.org\/10.18653\/v1\/p16-1231","key":"38_CR1","DOI":"10.18653\/v1\/p16-1231"},{"unstructured":"Carlini, N., Liu, C., Erlingsson, \u00da., Kos, J., Song, D.: The secret sharer: evaluating and testing unintended memorization in neural networks. In: USENIX Security Symposium (2019)","key":"38_CR2"},{"doi-asserted-by":"publisher","unstructured":"Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: S &P (2017). https:\/\/doi.org\/10.1109\/SP.2017.49","key":"38_CR3","DOI":"10.1109\/SP.2017.49"},{"doi-asserted-by":"publisher","unstructured":"Deng, L., Hinton, G.E., Kingsbury, B.: New types of deep neural network learning for speech recognition and related applications: an overview. In: ICASSP (2013). https:\/\/doi.org\/10.1109\/ICASSP.2013.6639344","key":"38_CR4","DOI":"10.1109\/ICASSP.2013.6639344"},{"doi-asserted-by":"publisher","unstructured":"Gowal, S., et al.: Scalable verified training for provably robust image classification. In: ICCV (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00494","key":"38_CR5","DOI":"10.1109\/ICCV.2019.00494"},{"unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Master\u2019s thesis, University of Toronto (2009)","key":"38_CR6"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)","key":"38_CR7"},{"unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial machine learning at scale. CoRR (2016). https:\/\/arxiv.org\/abs\/1611.01236","key":"38_CR8"},{"unstructured":"Leino, K., Fredrikson, M.: Stolen memories: leveraging model memorization for calibrated white-box membership inference. In: USENIX Security Symposium (2020). https:\/\/www.usenix.org\/conference\/usenixsecurity20\/presentation\/leino","key":"38_CR9"},{"unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018)","key":"38_CR10"},{"unstructured":"Mirman, M., Gehr, T., Vechev, M.T.: Differentiable abstract interpretation for provably robust neural networks. In: ICML (2018)","key":"38_CR11"},{"doi-asserted-by":"publisher","unstructured":"Papernot, N., McDaniel, P.D., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: EuroS &P (2016). https:\/\/doi.org\/10.1109\/EuroSP.2016.36","key":"38_CR12","DOI":"10.1109\/EuroSP.2016.36"},{"doi-asserted-by":"publisher","unstructured":"Pearson, K.: LIII. on lines and planes of closest fit to systems of points in space. London Edinburgh Dublin Philos. Mag. J. Sci. (1901). https:\/\/doi.org\/10.1080\/14786440109462720","key":"38_CR13","DOI":"10.1080\/14786440109462720"},{"doi-asserted-by":"crossref","unstructured":"Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., Backes, M.: ML-leaks: model and data independent membership inference attacks and defenses on machine learning models. In: NDSS (2019)","key":"38_CR14","DOI":"10.14722\/ndss.2019.23119"},{"doi-asserted-by":"publisher","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: CCS (2016). https:\/\/doi.org\/10.1145\/2976749.2978392","key":"38_CR15","DOI":"10.1145\/2976749.2978392"},{"doi-asserted-by":"publisher","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: S &P (2017). https:\/\/doi.org\/10.1109\/sp.2017.41","key":"38_CR16","DOI":"10.1109\/sp.2017.41"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015). https:\/\/arxiv.org\/abs\/1409.1556","key":"38_CR17"},{"unstructured":"Sinha, A., Namkoong, H., Duchi, J.C.: Certifying some distributional robustness with principled adversarial training. In: ICLR (2018)","key":"38_CR18"},{"unstructured":"Song, L., Mittal, P.: Systematic evaluation of privacy risks of machine learning models. In: USENIX Security Symposium (2021)","key":"38_CR19"},{"doi-asserted-by":"publisher","unstructured":"Song, L., Shokri, R., Mittal, P.: Privacy risks of securing machine learning models against adversarial examples. In: CCS (2019). https:\/\/doi.org\/10.1145\/3319535.3354211","key":"38_CR20","DOI":"10.1145\/3319535.3354211"},{"unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. CoRR (2017). https:\/\/arxiv.org\/abs\/1708.07747","key":"38_CR21"},{"doi-asserted-by":"publisher","unstructured":"Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy risk in machine learning: analyzing the connection to overfitting. In: IEEE CSF (2018). https:\/\/doi.org\/10.1109\/CSF.2018.00027","key":"38_CR22","DOI":"10.1109\/CSF.2018.00027"},{"unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E.P., Ghaoui, L.E., Jordan, M.I.: Theoretically principled trade-off between robustness and accuracy. In: ICML (2019). https:\/\/proceedings.mlr.press\/v97\/zhang19p.html","key":"38_CR23"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8429-9_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T03:22:07Z","timestamp":1703301727000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8429-9_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,24]]},"ISBN":["9789819984282","9789819984299"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8429-9_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,24]]},"assertion":[{"value":"24 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","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":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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)"}}]}}