{"id":"https://openalex.org/W2897320933","doi":"https://doi.org/10.1109/globalsip.2018.8646356","title":"ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM","display_name":"ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2897320933","doi":"https://doi.org/10.1109/globalsip.2018.8646356","mag":"2897320933"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2018.8646356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2018.8646356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/1721.1/137450","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114139431","display_name":"Tsui-Wei Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tsui-Wei Weng","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100356973","display_name":"Huan Zhang","orcid":"https://orcid.org/0000-0002-9914-6602"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huan Zhang","raw_affiliation_strings":["University of California, Los Angeles"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050344371","display_name":"Pin\u2010Yu Chen","orcid":"https://orcid.org/0000-0003-1039-8369"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pin-Yu Chen","raw_affiliation_strings":["IBM Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055054658","display_name":"Aur\u00e9lie Lozano","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aurelie Lozano","raw_affiliation_strings":["IBM Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010841999","display_name":"Cho\u2010Jui Hsieh","orcid":"https://orcid.org/0000-0002-3520-9627"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cho-Jui Hsieh","raw_affiliation_strings":["University of California, Los Angeles"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California, Los Angeles","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021651870","display_name":"Luca Daniel","orcid":"https://orcid.org/0000-0002-5880-3151"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luca Daniel","raw_affiliation_strings":["Massachusetts Institute of Technology, Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology, Cambridge","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1159","last_page":"1163"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9922000169754028,"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":0.9822999835014343,"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/robustness","display_name":"Robustness (evolution)","score":0.8365384340286255},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.6529609560966492},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6350613236427307},{"id":"https://openalex.org/keywords/lipschitz-continuity","display_name":"Lipschitz continuity","score":0.5124945640563965},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4877551198005676},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4612485468387604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44290637969970703},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25584250688552856}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8365384340286255},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.6529609560966492},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6350613236427307},{"id":"https://openalex.org/C22324862","wikidata":"https://www.wikidata.org/wiki/Q652707","display_name":"Lipschitz continuity","level":2,"score":0.5124945640563965},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4877551198005676},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4612485468387604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44290637969970703},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25584250688552856},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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":3,"locations":[{"id":"doi:10.1109/globalsip.2018.8646356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2018.8646356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:dspace.mit.edu:1721.1/137450","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/137450","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv","raw_type":"http://purl.org/eprint/type/ConferencePaper"},{"id":"pmh:oai:dspace.mit.edu:1721.1/137450.2","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/137450.2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv","raw_type":"http://purl.org/eprint/type/ConferencePaper"}],"best_oa_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/137450","is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/137450","pdf_url":null,"source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv","raw_type":"http://purl.org/eprint/type/ConferencePaper"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2243397390","https://openalex.org/W2594877703","https://openalex.org/W2618235498","https://openalex.org/W2620038827","https://openalex.org/W2746600820","https://openalex.org/W2765384636","https://openalex.org/W2768899812","https://openalex.org/W2797455600","https://openalex.org/W2798478264","https://openalex.org/W2962756933","https://openalex.org/W2963001136","https://openalex.org/W2963143631","https://openalex.org/W2963440492","https://openalex.org/W2963446712","https://openalex.org/W2963540169","https://openalex.org/W2963564844","https://openalex.org/W2963612069","https://openalex.org/W2963744840","https://openalex.org/W2963784236","https://openalex.org/W2963857521","https://openalex.org/W2964082701","https://openalex.org/W3106412272","https://openalex.org/W4294349862","https://openalex.org/W4295897591","https://openalex.org/W6711870810","https://openalex.org/W6734921443","https://openalex.org/W6738369334","https://openalex.org/W6744511767","https://openalex.org/W6745272055","https://openalex.org/W6748458661","https://openalex.org/W6748475379","https://openalex.org/W6750124314","https://openalex.org/W6750463028","https://openalex.org/W6750745550"],"related_works":["https://openalex.org/W1677518548","https://openalex.org/W3185441898","https://openalex.org/W4286857066","https://openalex.org/W3213958552","https://openalex.org/W4238239967","https://openalex.org/W4297812081","https://openalex.org/W2949926331","https://openalex.org/W2754545762","https://openalex.org/W4299562540","https://openalex.org/W3175273680"],"abstract_inverted_index":{"CLEVER":[0,66,81,91,115],"(Cross-Lipschitz":[1],"Extreme":[2,9],"Value":[3,10],"for":[4,16,41],"nEtwork":[5],"Robustness)":[6],"is":[7,63],"an":[8],"Theory":[11],"(EVT)":[12],"based":[13],"robustness":[14,31,39,57,62,96],"score":[15],"large-scale":[17],"deep":[18],"neural":[19,98],"networks":[20,99,105],"(DNNs).":[21],"In":[22],"this":[23,30],"paper,":[24],"we":[25,34,69],"propose":[26],"two":[27],"extensions":[28],"on":[29,53,120,126],"score.":[32,67],"First,":[33],"provide":[35],"a":[36,76,101,121],"new":[37,55],"formal":[38,56],"guarantee":[40,58],"classifier":[42],"functions":[43],"that":[44],"are":[45],"twice":[46],"differentiable.":[47],"We":[48,110],"apply":[49],"extreme":[50],"value":[51],"theory":[52],"the":[54,60,94,112,127],"and":[59],"estimated":[61],"called":[64],"second-order":[65],"Second,":[68],"discuss":[70],"how":[71],"to":[72],"handle":[73],"gradient":[74],"masking,":[75],"common":[77],"defensive":[78],"technique,":[79],"using":[80],"with":[82,106,116],"Backward":[83],"Pass":[84],"Differentiable":[85],"Approximation":[86],"(BPDA).":[87],"With":[88],"BPDA":[89,117],"applied,":[90],"can":[92],"evaluate":[93],"intrinsic":[95],"of":[97,100,114],"broader":[102],"class":[103],"-":[104],"non-differentiable":[107],"input":[108],"transformations.":[109],"demonstrate":[111],"effectiveness":[113],"in":[118],"experiments":[119],"121-layer":[122],"Densenet":[123],"model":[124],"trained":[125],"ImageNet":[128],"dataset.":[129]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}