{"id":"https://openalex.org/W4312429086","doi":"https://doi.org/10.1109/icpr56361.2022.9956280","title":"Detecting Compromised Architecture/Weights of a Deep Model","display_name":"Detecting Compromised Architecture/Weights of a Deep Model","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4312429086","doi":"https://doi.org/10.1109/icpr56361.2022.9956280"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956280","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5018188389","display_name":"James Beetham","orcid":null},"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":"James Beetham","raw_affiliation_strings":["University of Central Florida,Center for Research in Computer Vision","Center for Research in Computer Vision, University of Central Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Center for Research in Computer Vision","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"Center for Research in Computer Vision, University of Central Florida","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064519004","display_name":"Navid Kardan","orcid":null},"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":"Navid Kardan","raw_affiliation_strings":["University of Central Florida,Center for Research in Computer Vision","Center for Research in Computer Vision, University of Central Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Center for Research in Computer Vision","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"Center for Research in Computer Vision, University of Central Florida","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089986388","display_name":"Ajmal Mian","orcid":"https://orcid.org/0000-0002-5206-3842"},"institutions":[{"id":"https://openalex.org/I177877127","display_name":"The University of Western Australia","ror":"https://ror.org/047272k79","country_code":"AU","type":"education","lineage":["https://openalex.org/I177877127"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ajmal Mian","raw_affiliation_strings":["University of Western Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Western Australia","institution_ids":["https://openalex.org/I177877127"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103215307","display_name":"Mubarak Shah","orcid":"https://orcid.org/0000-0002-8216-1128"},"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":"Mubarak Shah","raw_affiliation_strings":["University of Central Florida,Center for Research in Computer Vision","Center for Research in Computer Vision, University of Central Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Center for Research in Computer Vision","institution_ids":["https://openalex.org/I106165777"]},{"raw_affiliation_string":"Center for Research in Computer Vision, University of Central Florida","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2843","last_page":"2849"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998000264167786,"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":0.9998000264167786,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9957000017166138,"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":0.6539880633354187},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.6324177980422974},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.37652239203453064},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3322729468345642},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07348635792732239}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6539880633354187},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.6324177980422974},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.37652239203453064},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3322729468345642},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07348635792732239},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956280","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956280","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/9e3a5e92-c495-4b41-8ebf-8bca9c4bfa5b","is_oa":false,"landing_page_url":"https://research-repository.uwa.edu.au/en/publications/9e3a5e92-c495-4b41-8ebf-8bca9c4bfa5b","pdf_url":null,"source":{"id":"https://openalex.org/S4306402492","display_name":"UWA Profiles and Research Repository (UWA)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177877127","host_organization_name":"The University of Western Australia","host_organization_lineage":["https://openalex.org/I177877127"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Beetham , J , Kardan , N , Mian , A & Shah , M 2022 , Detecting Compromised Architecture/Weights of a Deep Model . in 2022 26th International Conference on Pattern Recognition, ICPR 2022 . Proceedings - International Conference on Pattern Recognition , vol. 2022-August , IEEE, Institute of Electrical and Electronics Engineers , pp. 2843-2849 , 26th International Conference on Pattern Recognition, ICPR 2022 , Montreal , Canada , 21/08/22 . https://doi.org/10.1109/ICPR56361.2022.9956280","raw_type":"contributionToPeriodical"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5899999737739563}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1686810756","https://openalex.org/W1689711448","https://openalex.org/W1883420340","https://openalex.org/W1945616565","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2561975083","https://openalex.org/W2603766943","https://openalex.org/W2607219512","https://openalex.org/W2618043096","https://openalex.org/W2736067536","https://openalex.org/W2745565856","https://openalex.org/W2895097814","https://openalex.org/W2904693122","https://openalex.org/W2906208681","https://openalex.org/W2912447214","https://openalex.org/W2945033152","https://openalex.org/W2947133760","https://openalex.org/W2947874337","https://openalex.org/W2950782995","https://openalex.org/W2962847335","https://openalex.org/W2963062382","https://openalex.org/W2963070423","https://openalex.org/W2963158386","https://openalex.org/W2963361074","https://openalex.org/W2963857521","https://openalex.org/W2972986629","https://openalex.org/W2984699060","https://openalex.org/W2985451033","https://openalex.org/W3015625436","https://openalex.org/W3107235539","https://openalex.org/W3118608800","https://openalex.org/W3164228242","https://openalex.org/W3211999566","https://openalex.org/W4293584023","https://openalex.org/W4293846201","https://openalex.org/W4297573953","https://openalex.org/W4300511536","https://openalex.org/W6637162671","https://openalex.org/W6637373629","https://openalex.org/W6639568328","https://openalex.org/W6640425456","https://openalex.org/W6686164453","https://openalex.org/W6687483927","https://openalex.org/W6719080892","https://openalex.org/W6733645847","https://openalex.org/W6734483310","https://openalex.org/W6736207377","https://openalex.org/W6739868092","https://openalex.org/W6746608116","https://openalex.org/W6747819456","https://openalex.org/W6750404860","https://openalex.org/W6757042666","https://openalex.org/W6758648797","https://openalex.org/W6759580348","https://openalex.org/W6761833289","https://openalex.org/W6762970624","https://openalex.org/W6787972765","https://openalex.org/W6795416703"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2038503502"],"abstract_inverted_index":{"Adversarial":[0],"attacks":[1,98,133],"perturb":[2],"data":[3],"to":[4,41,80,92,100,107,121,145,166],"modify":[5],"a":[6,15,36,53,77,88,104,139],"model\u2019s":[7],"prediction.":[8],"These":[9],"perturbations":[10],"can":[11],"be":[12],"crafted":[13],"in":[14,109,119],"white-box":[16,45,60,97,130],"or":[17,29],"black-box":[18,62,101,132],"setting,":[19],"depending":[20],"on":[21,173],"whether":[22],"the":[23,42,69,73,82,94,111,114,123,126,147,150,156,169],"target":[24,74],"model":[25,37,55,75,79],"architecture/weights":[26],"are":[27],"known":[28],"unknown.":[30],"Compromised":[31],"architecture":[32],"and":[33,76,103,131,153,159],"weights":[34],"of":[35,72,96,113,125,142,149],"makes":[38],"it":[39,86],"vulnerable":[40],"more":[43],"powerful":[44],"attacks.":[46,64],"In":[47],"this":[48],"work,":[49],"we":[50],"determine":[51],"if":[52],"deep":[54],"is":[56],"compromised":[57],"by":[58],"distinguishing":[59],"from":[61],"adversarial":[63],"The":[65],"proposed":[66],"method":[67,144],"utilizes":[68],"internal":[70,127],"representations":[71,128],"proxy":[78,105,160],"increase":[81,122],"detector":[83],"efficacy.":[84],"Additionally,":[85],"employs":[87],"spatial":[89,157],"smoothing":[90,158],"module":[91,106],"control":[93],"strength":[95],"relative":[99],"attacks,":[102],"aid":[108],"measuring":[110],"transferability":[112],"attack.":[115],"Both":[116],"modules":[117],"work":[118],"tandem":[120],"contrast":[124],"between":[129],"for":[134],"better":[135],"discrimination.":[136],"We":[137],"perform":[138],"detailed":[140],"ablation":[141],"our":[143,164],"showcase":[146],"importance":[148],"different":[151],"modules,":[152],"show":[154],"that":[155],"defense":[161],"techniques":[162],"enable":[163],"framework":[165],"significantly":[167],"outperform":[168],"simple":[170],"classification":[171],"baseline":[172],"common":[174],"vision":[175],"datasets.":[176]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}