{"id":"https://openalex.org/W4386634478","doi":"https://doi.org/10.1109/access.2023.3314669","title":"Enhancing Black-Box Attacks With Self-Supervised Features and Luminance-Aware Visual Model","display_name":"Enhancing Black-Box Attacks With Self-Supervised Features and Luminance-Aware Visual Model","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386634478","doi":"https://doi.org/10.1109/access.2023.3314669"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3314669","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3314669","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10247507.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10247507.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088712137","display_name":"S. Xue","orcid":"https://orcid.org/0009-0006-9285-6229"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Siying Xue","raw_affiliation_strings":["School of Information Engineering, Wuhan University of Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Wuhan University of Technology, Wuhan, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101726779","display_name":"Wei Wu","orcid":"https://orcid.org/0000-0003-1781-5129"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wu","raw_affiliation_strings":["School of Information Engineering, Wuhan University of Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Wuhan University of Technology, Wuhan, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088712137"],"corresponding_institution_ids":["https://openalex.org/I196699116"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1118864,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":null,"first_page":"99765","last_page":"99775"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9998999834060669,"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":0.9998999834060669,"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":0.989799976348877,"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":0.984000027179718,"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/overfitting","display_name":"Overfitting","score":0.8791158199310303},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7840285301208496},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7489609718322754},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6478904485702515},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6270528435707092},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.5987164378166199},{"id":"https://openalex.org/keywords/black-box","display_name":"Black box","score":0.5852402448654175},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4576586186885834},{"id":"https://openalex.org/keywords/luminance","display_name":"Luminance","score":0.45556285977363586},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.44623076915740967},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.338923841714859},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23337090015411377},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19259881973266602},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12107178568840027}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8791158199310303},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7840285301208496},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7489609718322754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6478904485702515},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6270528435707092},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.5987164378166199},{"id":"https://openalex.org/C94966114","wikidata":"https://www.wikidata.org/wiki/Q29256","display_name":"Black box","level":2,"score":0.5852402448654175},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4576586186885834},{"id":"https://openalex.org/C73313986","wikidata":"https://www.wikidata.org/wiki/Q355386","display_name":"Luminance","level":2,"score":0.45556285977363586},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.44623076915740967},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.338923841714859},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23337090015411377},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19259881973266602},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12107178568840027},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3314669","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3314669","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10247507.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a1d1d21723664437a6191c35734597ee","is_oa":true,"landing_page_url":"https://doaj.org/article/a1d1d21723664437a6191c35734597ee","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 99765-99775 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3314669","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3314669","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10247507.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386634478.pdf","grobid_xml":"https://content.openalex.org/works/W4386634478.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W9657784","https://openalex.org/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W1950843348","https://openalex.org/W2117539524","https://openalex.org/W2145287260","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2412782625","https://openalex.org/W2570685808","https://openalex.org/W2603766943","https://openalex.org/W2607219512","https://openalex.org/W2618530766","https://openalex.org/W2620038827","https://openalex.org/W2774644650","https://openalex.org/W2889985731","https://openalex.org/W2913939497","https://openalex.org/W2962847335","https://openalex.org/W2962858109","https://openalex.org/W2963001136","https://openalex.org/W2963143631","https://openalex.org/W2963384482","https://openalex.org/W2963542245","https://openalex.org/W2963744840","https://openalex.org/W2963857521","https://openalex.org/W2964350391","https://openalex.org/W2969542116","https://openalex.org/W2972986629","https://openalex.org/W3005680577","https://openalex.org/W3034190247","https://openalex.org/W3035524453","https://openalex.org/W3035569111","https://openalex.org/W3035677743","https://openalex.org/W3099319035","https://openalex.org/W3103836116","https://openalex.org/W3145450063","https://openalex.org/W3167676691","https://openalex.org/W3179647175","https://openalex.org/W4221156702","https://openalex.org/W4226021007","https://openalex.org/W4293846201","https://openalex.org/W4312443924","https://openalex.org/W4312648479","https://openalex.org/W4313141826","https://openalex.org/W6620707391","https://openalex.org/W6631190155","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6731927902","https://openalex.org/W6736207377","https://openalex.org/W6739868092","https://openalex.org/W6745272055","https://openalex.org/W6748475379","https://openalex.org/W6774314701"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W3128011703","https://openalex.org/W4210532226"],"abstract_inverted_index":{"The":[0],"practical":[1],"utility":[2],"of":[3,28,73,87,138],"black-box":[4,17],"adversarial":[5,29,74,116],"attacks":[6,18],"in":[7,70,115],"deep":[8],"learning":[9],"security":[10],"has":[11],"gained":[12],"significant":[13,113],"attention.":[14],"However,":[15],"current":[16],"face":[19],"challenges":[20],"related":[21],"to":[22,25,99],"overfitting,":[23],"leading":[24],"limited":[26,37],"transferability":[27,72,118],"samples.":[30,75],"This":[31],"limitation":[32],"arises":[33],"from":[34],"excessive":[35],"iterations,":[36],"input":[38,101],"diversity":[39,102],"during":[40],"sample":[41,117],"generation,":[42],"and":[43,103,143],"indiscriminate":[44],"perturbation":[45,78],"addition.":[46],"To":[47],"address":[48],"these":[49],"issues,":[50],"this":[51],"paper":[52],"proposes":[53],"a":[54,62],"novel":[55],"approach.":[56],"Adversarial":[57],"perturbations":[58],"are":[59,81],"generated":[60,82],"using":[61,149],"self-supervised":[63],"model":[64],"with":[65,130],"strong":[66],"generalization":[67],"capabilities,":[68],"resulting":[69],"enhanced":[71],"Furthermore,":[76],"dynamic":[77],"range":[79],"maps":[80],"based":[83],"on":[84,108],"nonlinear":[85],"characteristics":[86],"human":[88],"eye":[89],"luminance":[90],"perception.":[91],"A":[92],"random":[93],"gamma":[94],"transform":[95],"is":[96],"also":[97],"introduced":[98],"increase":[100],"mitigate":[104],"overfitting.":[105],"Extensive":[106],"experiments":[107],"ImageNet":[109],"datasets":[110],"demonstrate":[111],"the":[112],"improvement":[114],"achieved":[119],"by":[120],"our":[121,124],"method.":[122],"Moreover,":[123],"approach":[125],"can":[126],"be":[127],"effectively":[128],"combined":[129],"other":[131],"techniques,":[132],"achieving":[133],"an":[134],"average":[135],"success":[136],"rate":[137],"91.70%":[139],"against":[140,145],"normal":[141],"models":[142,147],"74.90%":[144],"defensive":[146],"when":[148],"solely":[150],"normally":[151],"trained":[152],"models.":[153]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}