{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T02:08:02Z","timestamp":1779329282179,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819980727","type":"print"},{"value":"9789819980734","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T00:00:00Z","timestamp":1700006400000},"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\/978-981-99-8073-4_29","type":"book-chapter","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T08:02:54Z","timestamp":1699948974000},"page":"370-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SCME: A Self-contrastive Method for\u00a0Data-Free and\u00a0Query-Limited Model Extraction Attack"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7121-1257","authenticated-orcid":false,"given":"Renyang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9906-3508","authenticated-orcid":false,"given":"Jinhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7479-7970","authenticated-orcid":false,"given":"Kwok-Yan","family":"Lam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3004-7091","authenticated-orcid":false,"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5881-9436","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,15]]},"reference":[{"key":"29_CR1","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: ICML, vol. 119, pp. 1597\u20131607 (2020)"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Demuynck, K., Triefenbach, F.: Porting concepts from dnns back to gmms. In: ASRU, pp. 356\u2013361 (2013)","DOI":"10.1109\/ASRU.2013.6707756"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Dong, Y., Pang, T., Su, H., Zhu, J.: Evading defenses to transferable adversarial examples by translation-invariant attacks. In: CVPR, pp. 4312\u20134321 (2019)","DOI":"10.1109\/CVPR.2019.00444"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Duan, R., Ma, X., Wang, Y., Bailey, J., Qin, A.K., Yang, Y.: Adversarial camouflage: Hiding physical-world attacks with natural styles. In: CVPR, pp. 997\u20131005 (2020)","DOI":"10.1109\/CVPR42600.2020.00108"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Duan, R., et al.: Adversarial laser beam: Effective physical-world attack to dnns in a blink. In: CVPR, pp. 16062\u201316071 (2021)","DOI":"10.1109\/CVPR46437.2021.01580"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"29_CR7","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"29_CR8","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1106\u20131114 (2012)"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: ICLR (2017)","DOI":"10.1201\/9781351251389-8"},{"key":"29_CR10","unstructured":"Le, Y., Yang, X.S.: Tiny imagenet visual recognition challenge (2015)"},{"key":"29_CR11","unstructured":"LeCun, Y., Cortes, C., Burges, C.: Mnist handwritten digit database (1998)"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Liu, A., et al.: Perceptual-sensitive GAN for generating adversarial patches. In: AAAI, pp. 1028\u20131035 (2019)","DOI":"10.1609\/aaai.v33i01.33011028"},{"issue":"5","key":"29_CR13","first-page":"2209","volume":"18","author":"J Liu","year":"2021","unstructured":"Liu, J., Park, J.: Seeing is not always believing: detecting perception error attacks against autonomous vehicles. IEEE Trans. Dependable Sec. Comput. 18(5), 2209\u20132223 (2021)","journal-title":"IEEE Trans. Dependable Sec. Comput."},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Orekondy, T., Schiele, B., Fritz, M.: Knockoff nets: stealing functionality of black-box models. In: CVPR, pp. 4954\u20134963 (2019)","DOI":"10.1109\/CVPR.2019.00509"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P.D., Goodfellow, I.J.: Practical black-box attacks against machine learning. In: Asia@CCS, pp. 506\u2013519 (2017)","DOI":"10.1145\/3052973.3053009"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)","DOI":"10.1109\/ICCV.2015.314"},{"key":"29_CR17","unstructured":"Tram\u00e8r, F., Zhang, F., Juels, A., Reiter, M.K., Ristenpart, T.: Stealing machine learning models via prediction apis. In: USENIX Security, pp. 601\u2013618 (2016)"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Delving into data: effectively substitute training for black-box attack. In: CVPR, pp. 4761\u20134770 (2021)","DOI":"10.1109\/CVPR46437.2021.00473"},{"key":"29_CR19","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms (2017)"},{"key":"29_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102555","volume":"113","author":"M Yu","year":"2022","unstructured":"Yu, M., Sun, S.: Fe-dast: fast and effective data-free substitute training for black-box adversarial attacks. Comput. Sec. 113, 102555 (2022)","journal-title":"Comput. Sec."},{"issue":"5","key":"29_CR21","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1109\/TETCI.2022.3147508","volume":"6","author":"X Yuan","year":"2022","unstructured":"Yuan, X., Ding, L., Zhang, L., Li, X., Wu, D.O.: ES attack: model stealing against deep neural networks without data hurdles. IEEE Trans. Emerging Topics Comput. Intell. 6(5), 1258\u20131270 (2022)","journal-title":"IEEE Trans. Emerging Topics Comput. Intell."},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Towards efficient data free blackbox adversarial attack. In: CVPR, pp. 15094\u201315104 (2022)","DOI":"10.1109\/CVPR52688.2022.01469"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, M., Wu, J., Liu, Y., Liu, S., Zhu, C.: Dast: data-free substitute training for adversarial attacks. In: CVPR, pp. 231\u2013240 (2020)","DOI":"10.1109\/CVPR42600.2020.00031"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8073-4_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T23:38:07Z","timestamp":1730504287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8073-4_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,15]]},"ISBN":["9789819980727","9789819980734"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8073-4_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,15]]},"assertion":[{"value":"15 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}