{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:37:56Z","timestamp":1777549076100,"version":"3.51.4"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031253188","type":"print"},{"value":"9783031253195","type":"electronic"}],"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\/978-3-031-25319-5_3","type":"book-chapter","created":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T10:59:24Z","timestamp":1674903564000},"page":"45-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Practical Introduction to\u00a0Side-Channel Extraction of\u00a0Deep Neural Network Parameters"],"prefix":"10.1007","author":[{"given":"Rapha\u00ebl","family":"Joud","sequence":"first","affiliation":[]},{"given":"Pierre-Alain","family":"Mo\u00ebllic","sequence":"additional","affiliation":[]},{"given":"Simon","family":"Ponti\u00e9","sequence":"additional","affiliation":[]},{"given":"Jean-Baptiste","family":"Rigaud","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"key":"3_CR1","unstructured":"Batina, L., Bhasin, S., Jap, D., Picek, S.: CSI NN: reverse engineering of neural network architectures through electromagnetic side channel. In: 28th USENIX Security Symposium (USENIX Security 2019), pp. 515\u2013532 (2019)"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Breier, J., Jap, D., Hou, X., Bhasin, S., Liu, Y.: SNIFF: reverse engineering of neural networks with fault attacks. IEEE Trans. Reliab. (2021)","DOI":"10.1109\/TR.2021.3105697"},{"key":"3_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-030-56877-1_7","volume-title":"Advances in Cryptology \u2013 CRYPTO 2020","author":"N Carlini","year":"2020","unstructured":"Carlini, N., Jagielski, M., Mironov, I.: Cryptanalytic extraction of neural\u00a0network models. In: Micciancio, D., Ristenpart, T. (eds.) CRYPTO 2020. LNCS, vol. 12172, pp. 189\u2013218. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-56877-1_7"},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1049\/cit2.12026","volume":"6","author":"H Chabanne","year":"2021","unstructured":"Chabanne, H., Danger, J.L., Guiga, L., K\u00fchne, U.: Side channel attacks for architecture extraction of neural networks. CAAI Trans. Intell. Technol. 6(1), 3\u201316 (2021)","journal-title":"CAAI Trans. Intell. Technol."},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Dubey, A., Cammarota, R., Aysu, A.: MaskedNet: the first hardware inference engine aiming power side-channel protection. In: 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 197\u2013208 (2020)","DOI":"10.1109\/HOST45689.2020.9300276"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Dumont, M., Mo\u00ebllic, P.A., Viera, R., Dutertre, J.M., Bernhard, R.: An overview of laser injection against embedded neural network models. In: 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), pp. 616\u2013621. IEEE (2021)","DOI":"10.1109\/WF-IoT51360.2021.9595075"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Gongye, C., Fei, Y., Wahl, T.: Reverse-engineering deep neural networks using floating-point timing side-channels. In: 2020 57th ACM\/IEEE Design Automation Conference (DAC), pp. 1\u20136 (2020). ISSN 0738-100X","DOI":"10.1109\/DAC18072.2020.9218707"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Hua, W., Zhang, Z., Suh, G.E.: Reverse engineering convolutional neural networks through side-channel information leaks. In: 2018 55th ACM\/ESDA\/IEEE Design Automation Conference (DAC), pp. 1\u20136 (2018)","DOI":"10.1109\/DAC.2018.8465773"},{"key":"3_CR9","unstructured":"Jagielski, M., Carlini, N., Berthelot, D., Kurakin, A., Papernot, N.: High accuracy and high fidelity extraction of neural networks. In: 29th USENIX Security Symposium (USENIX Security 2020), pp. 1345\u20131362 (2020)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Joud, R., Mo\u00ebllic, P.A., Bernhard, R., Rigaud, J.B.: A review of confidentiality threats against embedded neural network models. In: 2021 IEEE 7th World Forum on Internet of Things (WF-IoT). IEEE (2021)","DOI":"10.1109\/WF-IoT51360.2021.9595434"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Maji, S., Banerjee, U., Chandrakasan, A.P.: Leaky nets: recovering embedded neural network models and inputs through simple power and timing side-channels-attacks and defenses. IEEE Internet of Things J. (2021)","DOI":"10.1109\/JIOT.2021.3061314"},{"issue":"15","key":"3_CR12","doi-asserted-by":"publisher","first-page":"6790","DOI":"10.3390\/app11156790","volume":"11","author":"M M\u00e9ndez Real","year":"2021","unstructured":"M\u00e9ndez Real, M., Salvador, R.: Physical side-channel attacks on embedded neural networks: a survey. Appl. Sci. 11(15), 6790 (2021)","journal-title":"Appl. Sci."},{"key":"3_CR13","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-030-28954-6_7","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"SJ Oh","year":"2019","unstructured":"Oh, S.J., Schiele, B., Fritz, M.: Towards reverse-engineering black-box neural networks. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 121\u2013144. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_7"},{"issue":"11","key":"3_CR14","first-page":"2717","volume":"67","author":"Y Xiang","year":"2020","unstructured":"Xiang, Y., Chen, Z., et al.: Open DNN box by power side-channel attack. IEEE Trans. Circuits Syst. II Express Briefs 67(11), 2717\u20132721 (2020)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Yu, H., Ma, H., Yang, K., Zhao, Y., Jin, Y.: DeepEM: deep neural networks model recovery through EM side-channel information leakage. In: 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 209\u2013218. IEEE (2020)","DOI":"10.1109\/HOST45689.2020.9300274"}],"container-title":["Lecture Notes in Computer Science","Smart Card Research and Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25319-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T11:22:35Z","timestamp":1674904955000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25319-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031253188","9783031253195"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25319-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CARDIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Smart Card Research and Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Birmingham","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"7 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cardis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/events.cs.bham.ac.uk\/cardis2022\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","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":"15","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":"52% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}