{"id":"https://openalex.org/W4312227646","doi":"https://doi.org/10.1109/lsp.2022.3229558","title":"Boosting Query Efficiency of Meta Attack With Dynamic Fine-Tuning","display_name":"Boosting Query Efficiency of Meta Attack With Dynamic Fine-Tuning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312227646","doi":"https://doi.org/10.1109/lsp.2022.3229558"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2022.3229558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2022.3229558","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"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 Signal Processing Letters","raw_type":"journal-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/A5100702459","display_name":"Da Lin","orcid":"https://orcid.org/0000-0001-7130-8756"},"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":true,"raw_author_name":"Da Lin","raw_affiliation_strings":["School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"affiliations":[{"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/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":["School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"affiliations":[{"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/A5011972758","display_name":"Weixuan Tang","orcid":"https://orcid.org/0000-0002-4082-1140"},"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":"Weixuan Tang","raw_affiliation_strings":["Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077333494","display_name":"Xiangui Kang","orcid":"https://orcid.org/0000-0002-3134-0353"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangui Kang","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100702459"],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":null,"apc_paid":null,"fwci":0.7954,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77232464,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"29","issue":null,"first_page":"2557","last_page":"2561"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9750999808311462,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9642999768257141,"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":0.8552213907241821},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.5244146585464478},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5036014914512634},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4957755506038666},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.4784812331199646},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4710623621940613},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.43864572048187256},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3891531527042389},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31359875202178955},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.15118399262428284},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.10070845484733582}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8552213907241821},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.5244146585464478},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5036014914512634},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4957755506038666},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.4784812331199646},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4710623621940613},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.43864572048187256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3891531527042389},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31359875202178955},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.15118399262428284},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.10070845484733582},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C194257627","wikidata":"https://www.wikidata.org/wiki/Q211554","display_name":"Amplifier","level":3,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2022.3229558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2022.3229558","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G1303975003","display_name":null,"funder_award_id":"62072484","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3206975426","display_name":null,"funder_award_id":"62272116","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5666409524","display_name":null,"funder_award_id":"62002075","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7706713251","display_name":null,"funder_award_id":"61872099","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2603766943","https://openalex.org/W2746600820","https://openalex.org/W2798302089","https://openalex.org/W2963163009","https://openalex.org/W2963857521","https://openalex.org/W2964205597","https://openalex.org/W2972986629","https://openalex.org/W3106412272","https://openalex.org/W3113355675","https://openalex.org/W3118608800","https://openalex.org/W4246193833","https://openalex.org/W4285148065","https://openalex.org/W4294646197","https://openalex.org/W6637162671","https://openalex.org/W6637373629","https://openalex.org/W6640425456","https://openalex.org/W6750254146","https://openalex.org/W6752985256","https://openalex.org/W6763511984","https://openalex.org/W6787972765"],"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/W2734358244","https://openalex.org/W4388700941","https://openalex.org/W3015200942"],"abstract_inverted_index":{"In":[0],"black-box":[1],"attack,":[2],"excessive":[3],"queries":[4,37,156,178],"to":[5,40,52,147],"target":[6,25],"model":[7,26],"may":[8],"cause":[9],"suspicion":[10],"and":[11,77,112,145,168,189],"expose":[12],"attacker's":[13],"identity.":[14],"Equipped":[15],"with":[16,27,45],"advanced":[17],"meta":[18],"learning":[19],"technique,":[20],"Meta":[21,81],"Attack":[22,82],"simulates":[23],"the":[24,33,42,63,67,70,85,93,96,107,110,114,121,124,128,172],"a":[28,46,74,79,136,184],"surrogate":[29],"model,":[30],"significantly":[31],"reducing":[32],"queries.":[34,55],"However,":[35],"it":[36],"for":[38,120],"ZOO-gradients":[39],"correct":[41],"estimated":[43,71],"meta-gradients":[44],"fixed":[47],"frequency,":[48],"thereby":[49],"still":[50],"leading":[51],"massive":[53],"unnecessary":[54],"To":[56],"overcome":[57],"this":[58,60,151],"limitation,":[59],"letter":[61],"takes":[62],"dynamic":[64,115,129],"changes":[65],"of":[66,69,87,100,109,117,127,138,142],"accuracy":[68,108],"gradients":[72],"as":[73],"starting":[75],"point,":[76],"develops":[78],"Dynamic":[80],"(DMA).":[83],"At":[84],"beginning":[86],"each":[88],"fine-tuning":[89,130],"round,":[90],"DMA":[91,153,174],"computes":[92],"distance":[94,103],"between":[95],"above":[97],"two":[98],"types":[99],"gradients.":[101],"Such":[102],"metric":[104],"can":[105,132],"reflect":[106],"meta-gradients,":[111],"guide":[113],"adjustment":[116],"query":[118,162],"frequency":[119],"ZOO-gradients.":[122],"Moreover,":[123],"working":[125],"flow":[126],"process":[131],"be":[133,148],"controlled":[134],"by":[135],"set":[137],"parameters,":[139],"which":[140],"are":[141],"physical":[143],"significance":[144],"easy":[146],"tuned.":[149],"By":[150],"means,":[152],"merely":[154],"launches":[155],"at":[157],"critical":[158],"moments,":[159],"greatly":[160],"saving":[161],"resource.":[163],"Experiments":[164],"conducted":[165],"on":[166],"MNIST":[167],"CIFAR10":[169],"show":[170],"that":[171],"proposed":[173],"requires":[175],"far":[176],"fewer":[177],"than":[179],"existing":[180],"methods":[181],"while":[182],"maintaining":[183],"satisfying":[185],"attack":[186],"success":[187],"rate":[188],"distortion.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}