{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T11:27:55Z","timestamp":1782300475584,"version":"3.54.5"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"MITRE Innovation Program"},{"name":"NSF","award":["1745302"],"award-info":[{"award-number":["1745302"]}]},{"name":"Analog Devices Fellowship"},{"name":"MathWorks Engineering Fellowship"},{"name":"DARPA DRINQS program","award":["D18AC00033"],"award-info":[{"award-number":["D18AC00033"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Emerg. Technol. Comput. Syst."],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:p>\n            This article presents a method for hardware trojan detection in integrated circuits. Unsupervised deep learning is used to classify wide field-of-view (4 \u00d7 4 mm\n            <jats:sup>2<\/jats:sup>\n            ), high spatial resolution magnetic field images taken using a Quantum Diamond Microscope (QDM). QDM magnetic imaging is enhanced using quantum control techniques and improved diamond material to increase magnetic field sensitivity by a factor of \u00a04 and measurement speed by a factor of \u00a016 over previous demonstrations. These upgrades facilitate the first demonstration of QDM magnetic field measurement for hardware trojan detection. Unsupervised convolutional neural networks and clustering are used to infer trojan presence from unlabeled data sets of 600 \u00d7 600 pixel magnetic field images without human bias. This analysis is shown to be more accurate than principal component analysis for distinguishing between field programmable gate arrays configured with trojan-free and trojan-inserted logic. This framework is tested on a set of scalable trojans that we developed and measured with the QDM. Scalable and TrustHub trojans are detectable down to a minimum trojan trigger size of 0.5% of the total logic. The trojan detection framework can be used for golden-chip-free detection, since knowledge of the chips\u2019 identities is only used to evaluate detection accuracy.\n          <\/jats:p>","DOI":"10.1145\/3531010","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T11:39:51Z","timestamp":1651232391000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Hardware Trojan Detection Using Unsupervised Deep Learning on Quantum Diamond Microscope Magnetic Field Images"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7621-2224","authenticated-orcid":false,"given":"Maitreyi","family":"Ashok","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8807-1361","authenticated-orcid":false,"given":"Matthew J.","family":"Turner","sequence":"additional","affiliation":[{"name":"University of Maryland, College Park, MD, USA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0311-4751","authenticated-orcid":false,"given":"Ronald L.","family":"Walsworth","sequence":"additional","affiliation":[{"name":"University of Maryland, College Park, MD, USA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0419-1863","authenticated-orcid":false,"given":"Edlyn V.","family":"Levine","sequence":"additional","affiliation":[{"name":"Harvard University, Cambridge, MA, USA and The MITRE Corporation, Bedford, MA, USA and University of Maryland, College Park, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5977-2748","authenticated-orcid":false,"given":"Anantha P.","family":"Chandrakasan","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSPEC.2008.4505310"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISEMC.2015.7256167"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature07278"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13389-013-0068-0"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2014.2334493"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1007\/978-3-030-00015-8_55","volume-title":"Cloud Computing and Security","author":"Bian Rongzhen","year":"2018","unstructured":"Rongzhen Bian, Mingfu Xue, and Jian Wang. 2018. 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