{"id":"https://openalex.org/W4411551124","doi":"https://doi.org/10.1109/cscwd64889.2025.11033255","title":"Enhancing Model Robustness and Accuracy via Learnable Adversarial Training","display_name":"Enhancing Model Robustness and Accuracy via Learnable Adversarial Training","publication_year":2025,"publication_date":"2025-05-05","ids":{"openalex":"https://openalex.org/W4411551124","doi":"https://doi.org/10.1109/cscwd64889.2025.11033255"},"language":"en","primary_location":{"id":"doi:10.1109/cscwd64889.2025.11033255","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","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/A5100661116","display_name":"Chunlong Fan","orcid":"https://orcid.org/0000-0001-8127-6292"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunlong Fan","raw_affiliation_strings":["Shenyang Aerospace University, Computer College,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University, Computer College,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027886883","display_name":"Wanyan Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanyan Guo","raw_affiliation_strings":["Shenyang Aerospace University, Computer College,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University, Computer College,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057904235","display_name":"Li Xu","orcid":"https://orcid.org/0009-0006-4083-639X"},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Xu","raw_affiliation_strings":["Shenyang Aerospace University, Computer College,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Shenyang Aerospace University, Computer College,Shenyang,China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100661116"],"corresponding_institution_ids":["https://openalex.org/I125904092"],"apc_list":null,"apc_paid":null,"fwci":2.8414,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91471878,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1501","last_page":"1506"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9926999807357788,"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":0.9926999807357788,"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":0.977400004863739,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9539999961853027,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8315502405166626},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.8247940540313721},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7028656005859375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5876951813697815},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4623737335205078},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34214168787002563}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8315502405166626},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8247940540313721},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7028656005859375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5876951813697815},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4623737335205078},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34214168787002563},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cscwd64889.2025.11033255","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cscwd64889.2025.11033255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1989400610","https://openalex.org/W2023284387","https://openalex.org/W2604505099","https://openalex.org/W2911588476","https://openalex.org/W2945375457","https://openalex.org/W2955863859","https://openalex.org/W2963118571","https://openalex.org/W2963165363","https://openalex.org/W2963516603","https://openalex.org/W2985282977","https://openalex.org/W2997560666","https://openalex.org/W3034215083","https://openalex.org/W3034368386","https://openalex.org/W3135970545","https://openalex.org/W3203790781","https://openalex.org/W4214585697","https://openalex.org/W4286609345","https://openalex.org/W4293846201","https://openalex.org/W4299401133","https://openalex.org/W4312593145","https://openalex.org/W4393404801","https://openalex.org/W4402137675","https://openalex.org/W4411245013"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"In":[0,39,106,158],"recent":[1],"years,":[2],"deep":[3],"learning":[4],"models":[5,98],"have":[6],"made":[7],"significant":[8],"advancements":[9],"in":[10,70],"enhancing":[11],"robustness":[12,69,81,153],"against":[13,82,116,154,168],"single-perturbation":[14],"adversarial":[15,59,74,88,149],"attacks,":[16],"such":[17],"as":[18],"<tex":[19,145],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[20,146],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\ell_{p}$</tex>-norm":[21],"attacks.":[22,84],"However,":[23],"the":[24,93,101,113,123,130],"development":[25],"of":[26,67,96,103,125],"defense":[27,115,167],"mechanisms":[28],"for":[29,177],"composite":[30,58,117,169],"attacks":[31],"involving":[32],"multiple":[33],"semantic":[34,54],"perturbations":[35,55,118,170],"remains":[36],"a":[37,44,63],"challenge.":[38],"this":[40],"paper,":[41],"we":[42],"propose":[43],"method":[45,142,161],"that":[46,138],"combines":[47],"projected":[48],"gradient":[49],"descent":[50],"(PGD)":[51],"with":[52],"sequential":[53],"to":[56,99],"generate":[57],"examples":[60],"(CAEs),":[61],"providing":[62],"more":[64],"comprehensive":[65],"evaluation":[66],"model":[68],"various":[71,155],"scenarios.":[72],"Existing":[73],"training":[75,89,102,141],"methods":[76],"primarily":[77],"focus":[78],"on":[79,129],"improving":[80],"single-type":[83],"We":[85],"introduce":[86],"learnable":[87],"(LAT),":[90],"which":[91],"leverages":[92],"classification":[94],"boundaries":[95],"clean":[97],"guide":[100],"robust":[104],"models.":[105],"contrast,":[107],"our":[108,139,160],"approach":[109],"not":[110],"only":[111],"enhances":[112],"model's":[114],"but":[119],"also":[120],"significantly":[121],"reduces":[122],"loss":[124],"natural":[126,172],"accuracy.":[127],"Experiments":[128],"CIFAR-10,":[131],"CIFAR-100":[132],"and":[133,171],"Tiny":[134],"ImageNet":[135],"datasets":[136],"show":[137],"proposed":[140],"outperforms":[143],"traditional":[144],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\ell_{\\infty}$</tex>-norm":[147],"boundary-based":[148],"training,":[150],"demonstrating":[151],"superior":[152],"attack":[156],"types.":[157],"summary,":[159],"strikes":[162],"an":[163],"optimal":[164],"balance":[165],"between":[166],"accuracy,":[173],"showing":[174],"strong":[175],"potential":[176],"practical":[178],"applications.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}