{"id":"https://openalex.org/W2965677914","doi":"https://doi.org/10.24963/ijcai.2019/654","title":"Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space","display_name":"Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2965677914","doi":"https://doi.org/10.24963/ijcai.2019/654","mag":"2965677914"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/654","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/654","pdf_url":"https://www.ijcai.org/proceedings/2019/0654.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0654.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101915882","display_name":"Linyi Li","orcid":"https://orcid.org/0000-0002-5403-3217"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Linyi Li","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034055494","display_name":"Zexuan Zhong","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zexuan Zhong","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374506","display_name":"Bo Li","orcid":"https://orcid.org/0000-0003-2083-9105"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Li","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048118068","display_name":"Tao Xie","orcid":"https://orcid.org/0000-0002-6731-216X"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tao Xie","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":0.9832,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.81822868,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4711","last_page":"4717"},"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.9241665601730347},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7267705798149109},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6869655251502991},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6854496002197266},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.6319472789764404},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.590192973613739},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.5705381631851196},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5381781458854675},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46218597888946533},{"id":"https://openalex.org/keywords/monotonic-function","display_name":"Monotonic function","score":0.43276840448379517},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36940616369247437},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34000760316848755},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3215615153312683},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2119453251361847}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.9241665601730347},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7267705798149109},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6869655251502991},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6854496002197266},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.6319472789764404},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.590192973613739},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5705381631851196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5381781458854675},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46218597888946533},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.43276840448379517},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36940616369247437},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34000760316848755},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3215615153312683},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2119453251361847},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/654","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/654","pdf_url":"https://www.ijcai.org/proceedings/2019/0654.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/654","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/654","pdf_url":"https://www.ijcai.org/proceedings/2019/0654.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3347738032","display_name":"CSR: Medium: Augmenting Logs with Static Analysis and Symbolic Execution","funder_award_id":"1564274","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3730466571","display_name":"TWC: Medium: Collaborative: Improving Mobile-Application Security via Text Analytics","funder_award_id":"1513939","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4139285423","display_name":"SHF: Small: Science and Tools for Intelligent Developer Testing","funder_award_id":"1816615","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2965677914.pdf","grobid_xml":"https://content.openalex.org/works/W2965677914.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2112796928","https://openalex.org/W2594877703","https://openalex.org/W2619479788","https://openalex.org/W2786163515","https://openalex.org/W2787708942","https://openalex.org/W2803850896","https://openalex.org/W2805807555","https://openalex.org/W2807040120","https://openalex.org/W2890472662","https://openalex.org/W2890505418","https://openalex.org/W2900153411","https://openalex.org/W2917875722","https://openalex.org/W2962943487","https://openalex.org/W2963143631","https://openalex.org/W2963207607","https://openalex.org/W2963440492","https://openalex.org/W2963496101","https://openalex.org/W2963564844","https://openalex.org/W2963857521","https://openalex.org/W2964082701","https://openalex.org/W2964153729","https://openalex.org/W2964197269","https://openalex.org/W3117881175","https://openalex.org/W3118608800","https://openalex.org/W4288600704","https://openalex.org/W4293846201"],"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":{"Machine":[0],"learning":[1],"techniques,":[2],"especially":[3],"deep":[4],"neural":[5],"networks":[6],"(DNNs),":[7],"have":[8,42],"been":[9,43],"widely":[10],"adopted":[11],"in":[12,75],"various":[13],"applications.":[14],"However,":[15],"DNNs":[16],"are":[17],"recently":[18],"found":[19],"to":[20,35,106,162,184,197],"be":[21,50],"vulnerable":[22],"against":[23],"adversarial":[24,96,153],"examples,":[25],"i.e.,":[26],"maliciously":[27],"perturbed":[28],"inputs":[29],"that":[30,145],"can":[31,49,148],"mislead":[32],"the":[33,87,95,103,108,112,124,130,133,138,163,179,192],"models":[34],"make":[36],"arbitrary":[37],"prediction":[38],"errors.":[39],"Empirical":[40],"defenses":[41,55],"studied,":[44],"but":[45],"many":[46],"of":[47,60,91,98,132],"them":[48],"adaptively":[51],"attacked":[52],"again.":[53],"Provable":[54],"provide":[56,149],"provable":[57,88,152],"error":[58,89,154,180,193],"bound":[59,64,90,181,194],"DNNs,":[61],"while":[62],"such":[63],"so":[65],"far":[66,68],"is":[67],"from":[69,182,195],"satisfaction.":[70],"To":[71],"address":[72],"this":[73,76],"issue,":[74],"paper,":[77],"we":[78,177,190],"present":[79],"our":[80,146],"approach":[81,147],"named":[82],"Robustra":[83],"for":[84],"effectively":[85],"improving":[86],"DNNs.":[92],"We":[93,116],"leverage":[94],"space":[97],"a":[99],"reference":[100,139],"model":[101],"as":[102],"feasible":[104],"region":[105],"solve":[107,117],"min-max":[109],"game":[110],"between":[111],"attackers":[113],"and":[114,128,158],"defenders.":[115],"its":[118],"dual":[119],"problem":[120],"by":[121,137,169],"linearly":[122],"approximating":[123],"attackers'":[125],"best":[126],"strategy":[127],"utilizing":[129],"monotonicity":[131],"slack":[134],"variables":[135],"introduced":[136],"model.":[140],"The":[141],"evaluation":[142],"results":[143],"show":[144],"significantly":[150],"better":[151],"bounds":[155],"on":[156,175],"MNIST":[157,176],"CIFAR10":[159],"datasets,":[160],"compared":[161],"state-of-the-art":[164],"results.":[165],"In":[166],"particular,":[167],"bounded":[168],"L^infty,":[170],"with":[171,186],"epsilon":[172,187],"=":[173,188],"0.1,":[174],"reduce":[178,191],"2.74%":[183],"2.09%;":[185],"0.3,":[189],"24.19%":[196],"16.91%.":[198]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}