{"id":"https://openalex.org/W4287884604","doi":"https://doi.org/10.1109/spw54247.2022.9833894","title":"Clairvoyance: Exploiting Far-field EM Emanations of GPU to \"See\" Your DNN Models through Obstacles at a Distance","display_name":"Clairvoyance: Exploiting Far-field EM Emanations of GPU to \"See\" Your DNN Models through Obstacles at a Distance","publication_year":2022,"publication_date":"2022-05-01","ids":{"openalex":"https://openalex.org/W4287884604","doi":"https://doi.org/10.1109/spw54247.2022.9833894"},"language":"en","primary_location":{"id":"doi:10.1109/spw54247.2022.9833894","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spw54247.2022.9833894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Security and Privacy Workshops (SPW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016451064","display_name":"Sisheng Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sisheng Liang","raw_affiliation_strings":["Clemson University,Clemson,SC,USA","Clemson University, Clemson, SC, USA"],"affiliations":[{"raw_affiliation_string":"Clemson University,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]},{"raw_affiliation_string":"Clemson University, Clemson, SC, USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010864491","display_name":"Zihao Zhan","orcid":"https://orcid.org/0000-0002-1467-9056"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zihao Zhan","raw_affiliation_strings":["University of Florida,Gainesville,FL,USA","University of Florida, Gainesville, FL, USA"],"affiliations":[{"raw_affiliation_string":"University of Florida,Gainesville,FL,USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033685295","display_name":"Fan Yao","orcid":"https://orcid.org/0000-0002-0360-5641"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fan Yao","raw_affiliation_strings":["University of Central Florida,Orlando,FL,USA","University of Central Florida, Orlando, FL, USA"],"affiliations":[{"raw_affiliation_string":"University of Central Florida,Orlando,FL,USA","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101731002","display_name":"Long Cheng","orcid":"https://orcid.org/0000-0003-1736-0873"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Long Cheng","raw_affiliation_strings":["Clemson University,Clemson,SC,USA","Clemson University, Clemson, SC, USA"],"affiliations":[{"raw_affiliation_string":"Clemson University,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]},{"raw_affiliation_string":"Clemson University, Clemson, SC, USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101696649","display_name":"Zhenkai Zhang","orcid":"https://orcid.org/0000-0002-9025-3460"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhenkai Zhang","raw_affiliation_strings":["Clemson University,Clemson,SC,USA","Clemson University, Clemson, SC, USA"],"affiliations":[{"raw_affiliation_string":"Clemson University,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]},{"raw_affiliation_string":"Clemson University, Clemson, SC, USA","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5016451064"],"corresponding_institution_ids":["https://openalex.org/I8078737"],"apc_list":null,"apc_paid":null,"fwci":0.9283,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.78574144,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"312","last_page":"322"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10951","display_name":"Cryptographic Implementations and Security","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8012852072715759},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7144981026649475},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5511248111724854},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5191715955734253},{"id":"https://openalex.org/keywords/near-and-far-field","display_name":"Near and far field","score":0.45483464002609253},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3921097218990326},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3296496272087097},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1463514268398285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8012852072715759},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7144981026649475},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5511248111724854},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5191715955734253},{"id":"https://openalex.org/C25227671","wikidata":"https://www.wikidata.org/wiki/Q13405516","display_name":"Near and far field","level":2,"score":0.45483464002609253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3921097218990326},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3296496272087097},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1463514268398285},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/spw54247.2022.9833894","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spw54247.2022.9833894","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Security and Privacy Workshops (SPW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1538973830","https://openalex.org/W1686810756","https://openalex.org/W1922655562","https://openalex.org/W1968902482","https://openalex.org/W2013066047","https://openalex.org/W2028870095","https://openalex.org/W2130942839","https://openalex.org/W2138383709","https://openalex.org/W2168500994","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2555618208","https://openalex.org/W2583993537","https://openalex.org/W2618530766","https://openalex.org/W2625110865","https://openalex.org/W2751592946","https://openalex.org/W2789304371","https://openalex.org/W2806082141","https://openalex.org/W2891810898","https://openalex.org/W2963303354","https://openalex.org/W2963844355","https://openalex.org/W2964350391","https://openalex.org/W3015367674","https://openalex.org/W3100944043","https://openalex.org/W3102836279","https://openalex.org/W3113745592","https://openalex.org/W3114482311","https://openalex.org/W3119909101","https://openalex.org/W3134495297","https://openalex.org/W3154803439","https://openalex.org/W4246001895","https://openalex.org/W4288057791","https://openalex.org/W6640090968","https://openalex.org/W6679436768","https://openalex.org/W6753937820","https://openalex.org/W6766787143","https://openalex.org/W6780010867"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W1923805083","https://openalex.org/W2373246155"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"(DNNs)":[3],"are":[4,13,63],"becoming":[5],"increasingly":[6],"popular":[7],"in":[8,70,77],"real-world":[9],"applications,":[10],"and":[11,61,139,142,153,164],"they":[12],"considered":[14],"valuable":[15],"assets":[16],"of":[17,24,112,137,148,151,155],"enterprises.":[18],"In":[19],"recent":[20],"years,":[21],"a":[22,82,88,103,110],"number":[23,136,147],"model":[25,43,68,90],"extraction":[26,44,91],"attacks":[27,45],"have":[28],"been":[29],"formulated":[30],"that":[31,95],"can":[32,129],"be":[33],"mounted":[34],"to":[35,50,57,105,166],"successfully":[36],"steal":[37,106],"proprietary":[38],"DNN":[39,107,132],"models.":[40],"Nevertheless,":[41],"previous":[42],"require":[46],"either":[47],"logical":[48],"access":[49,56],"the":[51,58,78,117,135,146],"target":[52],"models":[53,108],"or":[54],"physical":[55],"victim":[59,118],"machines,":[60],"thus":[62],"not":[64],"suitable":[65],"for":[66],"performing":[67],"stealing":[69],"scenarios":[71],"where":[72],"an":[73,127],"outside":[74],"attacker":[75,128],"is":[76],"proximity":[79],"but":[80],"at":[81,109],"distance.In":[83],"this":[84],"paper,":[85],"we":[86],"propose":[87],"new":[89],"attack":[92],"named":[93],"Clairvoyance":[94],"exploits":[96],"certain":[97],"far-field":[98],"electromagnetic":[99],"signals":[100],"emanated":[101],"from":[102,116],"GPU":[104],"distance":[111],"several":[113,159],"meters":[114],"away":[115],"machine":[119],"even":[120],"with":[121],"some":[122],"obstacles":[123],"in-between.":[124],"Using":[125],"Clairvoyance,":[126],"effectively":[130],"deduce":[131],"architectures":[133],"(e.g.,":[134,145,162],"layers":[138],"their":[140],"types)":[141],"layer":[143],"configurations":[144],"kernels,":[149],"sizes":[150,154],"layers,":[152],"strides).":[156],"We":[157],"use":[158],"case":[160],"studies":[161],"VGG":[163],"ResNet)":[165],"demonstrate":[167],"its":[168],"effectiveness.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}