{"id":"https://openalex.org/W3013829496","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207338","title":"Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries","display_name":"Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3013829496","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207338","mag":"3013829496"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207338","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5026873752","display_name":"Moritz Vinzent Seiler","orcid":"https://orcid.org/0000-0002-1750-9060"},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Moritz Seiler","raw_affiliation_strings":["Information Systems and Statistics, University of M\u00fcnster, M\u00fcnster, Germany"],"affiliations":[{"raw_affiliation_string":"Information Systems and Statistics, University of M\u00fcnster, M\u00fcnster, Germany","institution_ids":["https://openalex.org/I22465464"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084825454","display_name":"Heike Trautmann","orcid":"https://orcid.org/0000-0002-9788-8282"},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Heike Trautmann","raw_affiliation_strings":["Information Systems and Statistics, University of M\u00fcnster, M\u00fcnster, Germany"],"affiliations":[{"raw_affiliation_string":"Information Systems and Statistics, University of M\u00fcnster, M\u00fcnster, Germany","institution_ids":["https://openalex.org/I22465464"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008455153","display_name":"Pascal Kerschke","orcid":"https://orcid.org/0000-0003-2862-1418"},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pascal Kerschke","raw_affiliation_strings":["Information Systems and Statistics, University of M\u00fcnster, M\u00fcnster, Germany"],"affiliations":[{"raw_affiliation_string":"Information Systems and Statistics, University of M\u00fcnster, M\u00fcnster, Germany","institution_ids":["https://openalex.org/I22465464"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5026873752"],"corresponding_institution_ids":["https://openalex.org/I22465464"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03036129,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"37","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":1.0,"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":1.0,"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":0.9898999929428101,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9796000123023987,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.8231170177459717},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.6691030263900757},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6076806783676147},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5981003642082214},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5725762248039246},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5616276264190674},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.53679358959198},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5207012295722961},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4671924114227295},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.4561119079589844},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.44031578302383423}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8231170177459717},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.6691030263900757},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6076806783676147},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5981003642082214},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5725762248039246},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5616276264190674},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.53679358959198},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5207012295722961},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4671924114227295},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.4561119079589844},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44031578302383423},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207338","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207338","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W769612788","https://openalex.org/W1480376833","https://openalex.org/W1673923490","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1883420340","https://openalex.org/W1945616565","https://openalex.org/W1965555277","https://openalex.org/W1995341919","https://openalex.org/W1995562189","https://openalex.org/W2117539524","https://openalex.org/W2194775991","https://openalex.org/W2230740169","https://openalex.org/W2243397390","https://openalex.org/W2269778407","https://openalex.org/W2295107390","https://openalex.org/W2508156266","https://openalex.org/W2517229335","https://openalex.org/W2603766943","https://openalex.org/W2612637113","https://openalex.org/W2620038827","https://openalex.org/W2774644650","https://openalex.org/W2962835968","https://openalex.org/W2963003451","https://openalex.org/W2963143631","https://openalex.org/W2963154688","https://openalex.org/W2963207607","https://openalex.org/W2963389226","https://openalex.org/W2963464195","https://openalex.org/W2963467071","https://openalex.org/W2963542245","https://openalex.org/W2963744840","https://openalex.org/W2963857521","https://openalex.org/W2963911037","https://openalex.org/W2964040467","https://openalex.org/W2964153729","https://openalex.org/W2964253222","https://openalex.org/W3118608800","https://openalex.org/W4293846201","https://openalex.org/W4297752781","https://openalex.org/W6622262455","https://openalex.org/W6637162671","https://openalex.org/W6637373629","https://openalex.org/W6638444622","https://openalex.org/W6638667902","https://openalex.org/W6639568328","https://openalex.org/W6640425456","https://openalex.org/W6689238212","https://openalex.org/W6717255582","https://openalex.org/W6719080892","https://openalex.org/W6725508424","https://openalex.org/W6726407388","https://openalex.org/W6729756640","https://openalex.org/W6739868092","https://openalex.org/W6748475379"],"related_works":["https://openalex.org/W2950183588","https://openalex.org/W3080754722","https://openalex.org/W4383221314","https://openalex.org/W3093978547","https://openalex.org/W2953536436","https://openalex.org/W3203790781","https://openalex.org/W4313346231","https://openalex.org/W2738001131","https://openalex.org/W4285785480","https://openalex.org/W2997056298"],"abstract_inverted_index":{"Artificial":[0],"neural":[1,146],"networks":[2,8,27,42,147],"in":[3,9],"general":[4],"and":[5,15,150],"deep":[6],"learning":[7,18],"particular":[10],"established":[11,154],"themselves":[12],"as":[13,50,172],"popular":[14],"powerful":[16,77],"machine":[17],"algorithms.":[19],"While":[20],"the":[21,34,73,122,143,160],"often":[22],"tremendous":[23,35],"sizes":[24],"of":[25,37,75,117,124,145],"these":[26],"are":[28,128],"beneficial":[29],"when":[30],"solving":[31],"complex":[32],"tasks,":[33],"number":[36],"parameters":[38],"also":[39],"causes":[40],"such":[41,49],"to":[43,46,71,112,141,159,180],"be":[44,67,102],"vulnerable":[45],"malicious":[47],"behavior":[48],"adversarial":[51],"perturbations.":[52],"These":[53],"perturbations":[54],"can":[55,65,98],"change":[56],"a":[57,91,114,135,176],"model's":[58],"classification":[59],"decision.":[60],"Moreover,":[61],"while":[62],"single-step":[63],"adversaries":[64,79,96,149],"easily":[66,99],"transferred":[68,103],"from":[69],"network":[70],"network,":[72],"transfer":[74],"more":[76,170],"multi-step":[78],"has":[80],"-":[81,83],"usually":[82],"been":[84],"rather":[85],"difficult.In":[86],"this":[87],"work,":[88],"we":[89,133],"introduce":[90,134],"method":[92,108],"for":[93],"generating":[94],"strong":[95],"that":[97],"(and":[100],"frequently)":[101],"between":[104],"different":[105],"models.":[106],"This":[107],"is":[109,168],"then":[110],"used":[111],"generate":[113],"large":[115],"set":[116],"adversaries,":[118],"based":[119],"on":[120],"which":[121],"effects":[123],"selected":[125],"defense":[126,155,166],"methods":[127],"experimentally":[129],"assessed.":[130],"At":[131],"last,":[132],"novel,":[136],"simple,":[137],"yet":[138],"effective":[139],"approach":[140,167],"enhance":[142],"resilience":[144],"against":[148,153],"benchmark":[151],"it":[152,173],"methods.":[156],"In":[157],"contrast":[158],"already":[161],"existing":[162],"methods,":[163],"our":[164],"proposed":[165],"much":[169],"efficient":[171],"only":[174],"requires":[175],"single":[177],"additional":[178],"forward-pass":[179],"achieve":[181],"comparable":[182],"performance":[183],"results.":[184]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
