{"id":"https://openalex.org/W3164563754","doi":"https://doi.org/10.1007/s10489-023-04532-5","title":"Exploring misclassifications of robust neural networks to enhance adversarial attacks","display_name":"Exploring misclassifications of robust neural networks to enhance adversarial attacks","publication_year":2023,"publication_date":"2023-03-21","ids":{"openalex":"https://openalex.org/W3164563754","doi":"https://doi.org/10.1007/s10489-023-04532-5","mag":"3164563754"},"language":"en","primary_location":{"id":"doi:10.1007/s10489-023-04532-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-023-04532-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-023-04532-5.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10489-023-04532-5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028233502","display_name":"Leo Schwinn","orcid":"https://orcid.org/0000-0003-3967-2202"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Leo Schwinn","raw_affiliation_strings":["Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany"],"raw_orcid":"https://orcid.org/0000-0003-3967-2202","affiliations":[{"raw_affiliation_string":"Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085831135","display_name":"Ren\u00e9 Raab","orcid":"https://orcid.org/0000-0003-2035-3332"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ren\u00e9 Raab","raw_affiliation_strings":["Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053758786","display_name":"An Nguyen","orcid":"https://orcid.org/0000-0002-8759-7641"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"An Nguyen","raw_affiliation_strings":["Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023645490","display_name":"Dario Zanca","orcid":"https://orcid.org/0000-0001-5886-0597"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dario Zanca","raw_affiliation_strings":["Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014144494","display_name":"Bjoern M. Eskofier","orcid":"https://orcid.org/0000-0002-0417-0336"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bjoern Eskofier","raw_affiliation_strings":["Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department Artificial Intelligence in Biomedical Engineering, Friedirch-Alexander Universit\u00e4t Erlangen N\u00fcrnberg, Carl-Thiersch-Stra\u00dfe 2b, Erlangen, 91052, Bavaria, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5028233502"],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":6.9053,"has_fulltext":true,"cited_by_count":52,"citation_normalized_percentile":{"value":0.97446889,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"53","issue":"17","first_page":"19843","last_page":"19859"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9556999802589417,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials 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":0.9381999969482422,"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/adversarial-system","display_name":"Adversarial system","score":0.9193298816680908},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8254470825195312},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.759863018989563},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6200160384178162},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.560871422290802},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5595439076423645},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5557519197463989},{"id":"https://openalex.org/keywords/perturbation","display_name":"Perturbation (astronomy)","score":0.42595523595809937}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9193298816680908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8254470825195312},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.759863018989563},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6200160384178162},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.560871422290802},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5595439076423645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5557519197463989},{"id":"https://openalex.org/C177918212","wikidata":"https://www.wikidata.org/wiki/Q803623","display_name":"Perturbation (astronomy)","level":2,"score":0.42595523595809937},{"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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10489-023-04532-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-023-04532-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-023-04532-5.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10489-023-04532-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-023-04532-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-023-04532-5.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G353111149","display_name":null,"funder_award_id":"ES434/8-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320873","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57"},{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3164563754.pdf"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W1541288193","https://openalex.org/W2024060531","https://openalex.org/W2108598243","https://openalex.org/W2747329762","https://openalex.org/W2888630264","https://openalex.org/W2912237282","https://openalex.org/W2923292931","https://openalex.org/W2952730822","https://openalex.org/W2962729158","https://openalex.org/W2963143631","https://openalex.org/W2963857521","https://openalex.org/W2963894448","https://openalex.org/W2964153729","https://openalex.org/W2964253222","https://openalex.org/W2969249185","https://openalex.org/W2970115835","https://openalex.org/W2970316625","https://openalex.org/W2970680991","https://openalex.org/W2983219069","https://openalex.org/W2995245581","https://openalex.org/W2996140774","https://openalex.org/W2996344901","https://openalex.org/W2996564870","https://openalex.org/W2997532515","https://openalex.org/W3009559573","https://openalex.org/W3013967931","https://openalex.org/W3034758058","https://openalex.org/W3034994123","https://openalex.org/W3035032188","https://openalex.org/W3089774673","https://openalex.org/W3092171228","https://openalex.org/W3093235747","https://openalex.org/W3100593864","https://openalex.org/W3101114581","https://openalex.org/W3103340107","https://openalex.org/W3103385169","https://openalex.org/W3104620392","https://openalex.org/W3105604018","https://openalex.org/W3113092146","https://openalex.org/W3118608800","https://openalex.org/W3131055632","https://openalex.org/W3165333460","https://openalex.org/W3204300557","https://openalex.org/W4214585697","https://openalex.org/W4310895557","https://openalex.org/W6611608239"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W3093978547","https://openalex.org/W3203790781","https://openalex.org/W2997056298","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W3127875750","https://openalex.org/W4383221314","https://openalex.org/W2953536436"],"abstract_inverted_index":{"Abstract":[0],"Progress":[1],"in":[2,140,144],"making":[3,29],"neural":[4,49],"networks":[5,50],"more":[6],"robust":[7,54],"against":[8,55],"adversarial":[9,56,85,129,162],"attacks":[10,86,102,113,163,175],"is":[11,26,121],"mostly":[12],"marginal,":[13],"despite":[14],"the":[15,19,23,42,65,68,104,117,127],"great":[16],"efforts":[17],"of":[18,45,64,67,70,94,126,148],"research":[20],"community.":[21],"Moreover,":[22],"robustness":[24,69],"evaluation":[25],"often":[27],"imprecise,":[28],"it":[30],"challenging":[31],"to":[32,52,111,173],"identify":[33],"promising":[34],"approaches.":[35],"We":[36],"do":[37],"an":[38,145],"observational":[39],"study":[40],"on":[41,73,152],"classification":[43],"decisions":[44],"19":[46],"different":[47,95],"state-of-the-art":[48],"trained":[51],"be":[53],"attacks.":[57],"This":[58,109],"analysis":[59],"gives":[60],"a":[61,74,91,123,157],"new":[62],"indication":[63],"limits":[66],"current":[71,83],"models":[72],"common":[75],"benchmark.":[76],"In":[77],"addition,":[78],"our":[79],"findings":[80],"suggest":[81],"that":[82,100,135,164],"untargeted":[84],"induce":[87],"misclassification":[88],"toward":[89],"only":[90],"limited":[92],"amount":[93],"classes.":[96],"Similarly,":[97],"we":[98,133,155],"find":[99],"previous":[101],"under-explore":[103],"perturbation":[105,130],"space":[106],"during":[107],"optimization.":[108],"leads":[110],"unsuccessful":[112],"for":[114,161,176],"samples":[115],"where":[116],"initial":[118],"gradient":[119],"direction":[120],"not":[122],"good":[124],"approximation":[125],"final":[128],"direction.":[131],"Additionally,":[132],"observe":[134],"both":[136],"over-":[137],"and":[138,169],"under-confidence":[139],"model":[141,149],"predictions":[142],"result":[143],"inaccurate":[146],"assessment":[147],"robustness.":[150],"Based":[151],"these":[153],"observations,":[154],"propose":[156],"novel":[158],"loss":[159],"function":[160],"consistently":[165],"improves":[166],"their":[167],"efficiency":[168],"success":[170],"rate":[171],"compared":[172],"prior":[174],"all":[177],"30":[178],"analyzed":[179],"models.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
