{"id":"https://openalex.org/W4284898785","doi":"https://doi.org/10.1109/icip46576.2022.9897705","title":"Query-Efficient Adversarial Attack Based On Latin Hypercube Sampling","display_name":"Query-Efficient Adversarial Attack Based On Latin Hypercube Sampling","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4284898785","doi":"https://doi.org/10.1109/icip46576.2022.9897705"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897705","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897705","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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 IEEE International Conference on Image Processing (ICIP)","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/A5041128610","display_name":"Dan Wang","orcid":"https://orcid.org/0000-0003-4804-9578"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Wang","raw_affiliation_strings":["Guangzhou University,School of Computer Science and Cyber Engineering,Guangzhou,China","School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,School of Computer Science and Cyber Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008333363","display_name":"Jiayu Lin","orcid":"https://orcid.org/0000-0002-4759-0410"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayu Lin","raw_affiliation_strings":["Guangzhou University,School of Computer Science and Cyber Engineering,Guangzhou,China","School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,School of Computer Science and Cyber Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064709384","display_name":"Yuan\u2010Gen Wang","orcid":"https://orcid.org/0000-0003-3010-4196"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan-Gen Wang","raw_affiliation_strings":["Guangzhou University,School of Computer Science and Cyber Engineering,Guangzhou,China","School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,School of Computer Science and Cyber Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":null,"apc_paid":null,"fwci":1.8556,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.87598307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"546","last_page":"550"},"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":0.9905999898910522,"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":0.9757000207901001,"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/latin-hypercube-sampling","display_name":"Latin hypercube sampling","score":0.8605182766914368},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7537885904312134},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7059414386749268},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5869351625442505},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5243023633956909},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.44068458676338196},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.4299643337726593},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.4142847955226898},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40403637290000916},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3473546504974365},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2627450227737427},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2073308229446411},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1972399353981018},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14499935507774353},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.11661076545715332}],"concepts":[{"id":"https://openalex.org/C20820323","wikidata":"https://www.wikidata.org/wiki/Q6496514","display_name":"Latin hypercube sampling","level":3,"score":0.8605182766914368},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7537885904312134},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7059414386749268},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5869351625442505},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5243023633956909},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.44068458676338196},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.4299643337726593},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.4142847955226898},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40403637290000916},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3473546504974365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2627450227737427},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2073308229446411},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1972399353981018},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14499935507774353},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.11661076545715332},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897705","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897705","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","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 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.5199999809265137}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320328715","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2108598243","https://openalex.org/W2302255633","https://openalex.org/W2535873859","https://openalex.org/W2746600820","https://openalex.org/W2774644650","https://openalex.org/W2798302089","https://openalex.org/W2963446712","https://openalex.org/W2963857521","https://openalex.org/W2977099891","https://openalex.org/W3015625436","https://openalex.org/W3106412272","https://openalex.org/W4243645092","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6731039222","https://openalex.org/W6746608116","https://openalex.org/W6750404860","https://openalex.org/W6752985256","https://openalex.org/W6787972765"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W4388724521","https://openalex.org/W3014182917","https://openalex.org/W2120319877","https://openalex.org/W4385973337","https://openalex.org/W2766254559","https://openalex.org/W3042131354","https://openalex.org/W4212763939","https://openalex.org/W1968861439"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,39,67,95],"be":[3],"applicable":[4],"in":[5,130],"real-world":[6],"scenario,":[7],"Boundary":[8,64],"Attacks":[9],"(BAs)":[10],"were":[11],"proposed":[12,123],"and":[13,114],"ensured":[14],"one":[15],"hundred":[16],"percent":[17],"attack":[18],"success":[19],"rate":[20],"with":[21,72],"only":[22],"decision":[23],"information.":[24],"However,":[25],"existing":[26],"BA":[27,128],"methods":[28,129],"craft":[29],"adversarial":[30],"examples":[31],"by":[32,102],"leveraging":[33],"a":[34,44,59],"simple":[35],"random":[36,84,91],"sampling":[37],"(SRS)":[38],"estimate":[40],"the":[41,52,79,87,96,119,122,126],"gradient,":[42],"consuming":[43],"large":[45],"number":[46,82],"of":[47,54,83,121,132],"model":[48],"queries.":[49],"To":[50],"overcome":[51],"drawback":[53],"SRS,":[55,73],"this":[56],"paper":[57],"proposes":[58],"Latin":[60],"Hypercube":[61],"Sampling":[62],"based":[63],"Attack":[65],"(LHS-BA)":[66],"save":[68],"query":[69,133],"budget.":[70],"Compared":[71],"LHS":[74],"has":[75],"better":[76],"uniformity":[77],"under":[78],"same":[80],"limited":[81],"samples.":[85],"Therefore,":[86],"average":[88],"on":[89,108],"these":[90],"samples":[92],"is":[93],"closer":[94],"true":[97],"gradient":[98],"than":[99],"that":[100],"estimated":[101],"SRS.":[103],"Various":[104],"experiments":[105],"are":[106,138],"conducted":[107],"benchmark":[109],"datasets":[110],"including":[111],"MNIST,":[112],"CIFAR,":[113],"ImageNet-1K.":[115],"Experimental":[116],"results":[117],"demonstrate":[118],"superiority":[120],"LHS-BA":[124],"over":[125],"state-of-the-art":[127],"terms":[131],"efficiency.":[134],"The":[135],"source":[136],"codes":[137],"publicly":[139],"available":[140],"at":[141],"https://github.com/GZHU-DVL/LHS-BA.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
