{"id":"https://openalex.org/W4387968741","doi":"https://doi.org/10.1145/3581783.3612092","title":"Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks","display_name":"Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387968741","doi":"https://doi.org/10.1145/3581783.3612092"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3612092","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","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/A5100419618","display_name":"Jun Guo","orcid":"https://orcid.org/0000-0002-6626-4135"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Guo","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084838055","display_name":"Xingyu Zheng","orcid":"https://orcid.org/0009-0009-6283-7635"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingyu Zheng","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014870180","display_name":"Aishan Liu","orcid":"https://orcid.org/0000-0002-4224-1318"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aishan Liu","raw_affiliation_strings":["NLSDE, Beihang University &amp; Institute of Dataspace, Beijing, China"],"affiliations":[{"raw_affiliation_string":"NLSDE, Beihang University &amp; Institute of Dataspace, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081392445","display_name":"Siyuan Liang","orcid":"https://orcid.org/0000-0002-6154-0233"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuan Liang","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076884053","display_name":"Yisong Xiao","orcid":"https://orcid.org/0000-0001-8227-0052"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yisong Xiao","raw_affiliation_strings":["Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101584415","display_name":"Yi-Chao Wu","orcid":"https://orcid.org/0000-0001-9628-4308"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yichao Wu","raw_affiliation_strings":["SenseTime Group Limited, Beijing, China"],"affiliations":[{"raw_affiliation_string":"SenseTime Group Limited, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xianglong Liu","orcid":"https://orcid.org/0000-0002-7618-3275"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianglong Liu","raw_affiliation_strings":["NLSDE, Beihang University, Zhongguancun Laboratory, &amp; Institute of Dataspace, Beijing &amp; Hefei, China"],"affiliations":[{"raw_affiliation_string":"NLSDE, Beihang University, Zhongguancun Laboratory, &amp; Institute of Dataspace, Beijing &amp; Hefei, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100419618"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":1.3826,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.85202158,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4178","last_page":"4189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.994700014591217,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9714999794960022,"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":0.8414250612258911},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7549737691879272},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6796319484710693},{"id":"https://openalex.org/keywords/adversary","display_name":"Adversary","score":0.6310304999351501},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6294316649436951},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5834599733352661},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.4622798264026642},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4221286475658417},{"id":"https://openalex.org/keywords/isolation","display_name":"Isolation (microbiology)","score":0.41983261704444885},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.2727656960487366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8414250612258911},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7549737691879272},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6796319484710693},{"id":"https://openalex.org/C41065033","wikidata":"https://www.wikidata.org/wiki/Q2825412","display_name":"Adversary","level":2,"score":0.6310304999351501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6294316649436951},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5834599733352661},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.4622798264026642},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4221286475658417},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.41983261704444885},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2727656960487366},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C89423630","wikidata":"https://www.wikidata.org/wiki/Q7193","display_name":"Microbiology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3612092","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3612092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8100000023841858,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2376276132","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G489730147","display_name":null,"funder_award_id":"62206009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5353313430","display_name":null,"funder_award_id":"62022009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5399121702","display_name":null,"funder_award_id":"2022009","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6192609413","display_name":null,"funder_award_id":"2022009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8720158838","display_name":null,"funder_award_id":"62022009, 62206009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320326978","display_name":"State Key Laboratory of Software Development Environment","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2051267297","https://openalex.org/W2112796928","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2296452361","https://openalex.org/W2594481151","https://openalex.org/W2603766943","https://openalex.org/W2747329762","https://openalex.org/W2963303354","https://openalex.org/W2963560987","https://openalex.org/W2963844355","https://openalex.org/W2973414778","https://openalex.org/W2991526369","https://openalex.org/W2999905431","https://openalex.org/W3035379805","https://openalex.org/W3087685156","https://openalex.org/W3106539628","https://openalex.org/W3107530881","https://openalex.org/W3113162324","https://openalex.org/W3169253336","https://openalex.org/W3174136778","https://openalex.org/W3178659068","https://openalex.org/W3179216274","https://openalex.org/W3185095134","https://openalex.org/W4224874620","https://openalex.org/W4225999490","https://openalex.org/W4229449242","https://openalex.org/W4285412155","https://openalex.org/W4299301436","https://openalex.org/W4304080321","https://openalex.org/W4312343407","https://openalex.org/W4386076210","https://openalex.org/W6600297362","https://openalex.org/W6601289607","https://openalex.org/W6604233986","https://openalex.org/W6771876938"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W4386603768","https://openalex.org/W2886711096","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W2590796488","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W4283319738"],"abstract_inverted_index":{"Despite":[0],"the":[1,23,28,37,48,54,79,88,126,131,138,152,168,182,197],"broad":[2],"application":[3],"of":[4,36,63,81,108,213],"Machine":[5],"Learning":[6],"models":[7,83,157,177],"as":[8],"a":[9,97,121],"Service":[10],"(MLaaS),":[11],"they":[12],"are":[13,59],"vulnerable":[14],"to":[15,47,52,144,158,170,201,210],"model":[16,24,104,123,148],"stealing":[17,42,75,105,163,205],"attacks.":[18],"These":[19],"attacks":[20],"can":[21,135,166,222],"replicate":[22],"functionality":[25],"by":[26,124],"using":[27],"black-box":[29],"query":[30],"process":[31],"without":[32],"any":[33],"prior":[34],"knowledge":[35,174],"target":[38],"victim":[39,176],"model.":[40],"Existing":[41],"defenses":[43,58],"add":[44],"deceptive":[45],"perturbations":[46,146],"victim's":[49],"posterior":[50],"probabilities":[51],"mislead":[53],"attackers.":[55],"However,":[56],"these":[57],"now":[60],"suffering":[61],"problems":[62],"high":[64],"inference":[65,116,139],"computational":[66,140],"overheads":[67],"and":[68,74,94,99,194,207],"unfavorable":[69],"trade-offs":[70],"between":[71],"benign":[72,153,183],"accuracy":[73],"robustness,":[76],"which":[77,134,165],"challenges":[78],"feasibility":[80],"deployed":[82],"in":[84,225],"practice.":[85],"To":[86],"address":[87],"problems,":[89],"this":[90],"paper":[91],"proposes":[92],"Isolation":[93],"Induction":[95],"(InI),":[96],"novel":[98],"effective":[100],"training":[101,128],"framework":[102],"for":[103],"defenses.":[106],"Instead":[107],"deploying":[109],"auxiliary":[110],"defense":[111],"modules":[112],"that":[113,150],"introduce":[114],"redundant":[115],"time,":[117],"InI":[118,215],"directly":[119],"trains":[120],"defensive":[122],"isolating":[125],"adversary's":[127],"gradient":[129],"from":[130,175],"expected":[132],"gradient,":[133],"effectively":[136],"reduce":[137],"cost.":[141],"In":[142],"contrast":[143],"adding":[145],"over":[147,216],"predictions":[149],"harm":[151],"accuracy,":[154],"we":[155],"train":[156],"produce":[159],"uninformative":[160],"outputs":[161],"against":[162],"queries,":[164],"induce":[167],"adversary":[169],"extract":[171],"little":[172],"useful":[173],"with":[178],"minimal":[179],"impact":[180],"on":[181,187,204],"performance.":[184],"Extensive":[185],"experiments":[186],"several":[188],"visual":[189],"classification":[190],"datasets":[191],"(e.g.,":[192],"MNIST":[193],"CIFAR10)":[195],"demonstrate":[196],"superior":[198],"robustness":[199],"(up":[200,209],"48%":[202],"reduction":[203],"accuracy)":[206],"speed":[208],"25.4\u00d7":[211],"faster)":[212],"our":[214],"other":[217],"state-of-the-art":[218],"methods.":[219],"Our":[220],"codes":[221],"be":[223],"found":[224],"https://github.com/DIG-Beihang/InI-Model-Stealing-Defense.":[226]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
