{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:10:55Z","timestamp":1773155455872,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031171420","type":"print"},{"value":"9783031171437","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\/978-3-031-17143-7_18","type":"book-chapter","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T04:04:22Z","timestamp":1663905862000},"page":"364-383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Precise Extraction of\u00a0Deep Learning Models via\u00a0Side-Channel Attacks on\u00a0Edge\/Endpoint Devices"],"prefix":"10.1007","author":[{"given":"Younghan","family":"Lee","sequence":"first","affiliation":[]},{"given":"Sohee","family":"Jun","sequence":"additional","affiliation":[]},{"given":"Yungi","family":"Cho","sequence":"additional","affiliation":[]},{"given":"Woorim","family":"Han","sequence":"additional","affiliation":[]},{"given":"Hyungon","family":"Moon","sequence":"additional","affiliation":[]},{"given":"Yunheung","family":"Paek","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"18_CR1","unstructured":"Amazon: Amazon AmazonSageMaker. https:\/\/docs.aws.amazon.com\/sagemaker\/index.html (2021). Accessed 15 Nov 2021"},{"key":"18_CR2","unstructured":"Barbalau, A., Cosma, A., Ionescu, R.T., Popescu, M.: Black-box ripper: copying black-box models using generative evolutionary algorithms. arXiv preprint arXiv:2010.11158 (2020)"},{"key":"18_CR3","unstructured":"Beatrice, A.: Top companies using machine learning in a profitable way, August 2021. https:\/\/www.analyticsinsight.net\/top-companies-using-machine-learning-in-a-profitable-way\/. Accessed 23 Aug 2021"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Correia-Silva, J.R., Berriel, R.F., Badue, C., de Souza, A.F., Oliveira-Santos, T.: Copycat CNN: stealing knowledge by persuading confession with random non-labeled data. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489592"},{"key":"18_CR5","unstructured":"Google: Google ML Engine. https:\/\/cloud.google.com (2021). Accessed 15 Nov 2021"},{"key":"18_CR6","unstructured":"Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset (2007)"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Hu, X., et al.: DeepSniffer: a DNN model extraction framework based on learning architectural hints. In: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 385\u2013399 (2020)","DOI":"10.1145\/3373376.3378460"},{"key":"18_CR9","unstructured":"Krizhevsky, A., et al.: Learning multiple layers of features from tiny images (2009)"},{"issue":"7","key":"18_CR10","doi-asserted-by":"publisher","first-page":"1956","DOI":"10.1007\/s11263-020-01316-z","volume":"128","author":"A Kuznetsova","year":"2020","unstructured":"Kuznetsova, A., et al.: The open images dataset v4. Int. J. Comput. Vision 128(7), 1956\u20131981 (2020)","journal-title":"Int. J. Comput. Vision"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Li, D., Wang, X., Kong, D.: DeepreBirth: accelerating deep neural network execution on mobile devices. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11876"},{"key":"18_CR12","unstructured":"Lorica, B., Paco, N.: The State of Machine Learning Adoption in the Enterprise. O\u2019Reilly Media, Sebastopol (2018)"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Muhammad, M.B., Yeasin, M.: Eigen-CAM: class activation map using principal components. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206626"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Orekondy, T., Schiele, B., Fritz, M.: Knockoff nets: stealing functionality of black-box models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4954\u20134963 (2019)","DOI":"10.1109\/CVPR.2019.00509"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Pal, S., Gupta, Y., Shukla, A., Kanade, A., Shevade, S., Ganapathy, V.: ActiveThief: model extraction using active learning and unannotated public data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 865\u2013872 (2020)","DOI":"10.1609\/aaai.v34i01.5432"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against machine learning. In: Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security, pp. 506\u2013519 (2017)","DOI":"10.1145\/3052973.3053009"},{"key":"18_CR17","unstructured":"Q-engineering: Deep learning with raspberry pi and alternatives in 2022 (2022). https:\/\/qengineering.eu\/deep-learning-with-raspberry-pi-and-alternatives.html. Accessed 11 Apr 2022"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 413\u2013420. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206537"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Ribeiro, M., Grolinger, K., Capretz, M.A.: MLaaS: machine learning as a service. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 896\u2013902. IEEE (2015)","DOI":"10.1109\/ICMLA.2015.152"},{"issue":"3","key":"18_CR20","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"18_CR21","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"18_CR22","unstructured":"Tram\u00e8r, F., Zhang, F., Juels, A., Reiter, M.K., Ristenpart, T.: Stealing machine learning models via prediction APIS. In: 25th USENIX Security Symposium (USENIX Security 2016), pp. 601\u2013618 (2016)"},{"key":"18_CR23","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset (2011)"},{"key":"18_CR24","unstructured":"Yan, M., Fletcher, C.W., Torrellas, J.: Cache telepathy: leveraging shared resource attacks to learn DNN architectures. In: 29th USENIX Security Symposium (USENIX Security 2020), pp. 2003\u20132020 (2020)"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Yu, H., Yang, K., Zhang, T., Tsai, Y.Y., Ho, T.Y., Jin, Y.: Cloudleak: large-scale deep learning models stealing through adversarial examples. In: NDSS (2020)","DOI":"10.14722\/ndss.2020.24178"},{"key":"18_CR26","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)","DOI":"10.5244\/C.30.87"},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Reiter, M.K.: D\u00fcppel: retrofitting commodity operating systems to mitigate cache side channels in the cloud. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 827\u2013838 (2013)","DOI":"10.1145\/2508859.2516741"},{"key":"18_CR28","unstructured":"Zhang, X., Wang, Q., Zaheer, C.: OpenBLAS (2019). https:\/\/www.openblas.net\/"}],"container-title":["Lecture Notes in Computer Science","Computer Security \u2013 ESORICS 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17143-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T04:08:52Z","timestamp":1663906132000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17143-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031171420","9783031171437"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17143-7_18","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":"24 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESORICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Symposium on Research in Computer Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Copenhagen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","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":"26 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esorics2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/esorics2022.compute.dtu.dk\/","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":"562","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":"104","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":"6","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":"3.4","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":"12","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)"}}]}}