{"id":"https://openalex.org/W4403556737","doi":"https://doi.org/10.48550/arxiv.2409.03646","title":"Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG","display_name":"Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG","publication_year":2024,"publication_date":"2024-09-05","ids":{"openalex":"https://openalex.org/W4403556737","doi":"https://doi.org/10.48550/arxiv.2409.03646"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2409.03646","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.03646","pdf_url":"https://arxiv.org/pdf/2409.03646","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2409.03646","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053082011","display_name":"Manshan Guo","orcid":"https://orcid.org/0000-0002-5506-6854"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Guo, Manshan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069173567","display_name":"Bhavin Choksi","orcid":"https://orcid.org/0000-0002-6475-4149"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choksi, Bhavin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054800803","display_name":"Sari Saba-Sadiya","orcid":"https://orcid.org/0009-0005-7482-3274"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sadiya, Sari","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065865650","display_name":"Alessandro T. Gifford","orcid":"https://orcid.org/0000-0002-8923-9477"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gifford, Alessandro T.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054185529","display_name":"Martina G. Vilas","orcid":"https://orcid.org/0000-0002-1097-8534"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vilas, Martina G.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089543782","display_name":"Radoslaw Martin Cichy","orcid":"https://orcid.org/0000-0003-4190-6071"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cichy, Radoslaw M.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102863049","display_name":"Gemma Roig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roig, Gemma","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5053082011"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9972000122070312,"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":0.9972000122070312,"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":0.9455000162124634,"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/robustness","display_name":"Robustness (evolution)","score":0.7622281312942505},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7415287494659424},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6079275608062744},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5906274914741516},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5726403594017029},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.49910402297973633},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.46409574151039124},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4324336647987366},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40536990761756897},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3861515522003174},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.3558879494667053},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35293757915496826},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.33599480986595154},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.13169175386428833},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06392022967338562},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.06310856342315674}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7622281312942505},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7415287494659424},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6079275608062744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5906274914741516},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5726403594017029},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.49910402297973633},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.46409574151039124},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4324336647987366},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40536990761756897},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3861515522003174},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3558879494667053},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35293757915496826},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.33599480986595154},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.13169175386428833},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06392022967338562},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.06310856342315674},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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":4,"locations":[{"id":"pmh:oai:arXiv.org:2409.03646","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.03646","pdf_url":"https://arxiv.org/pdf/2409.03646","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:publikationen.ub.uni-frankfurt.de:95733","is_oa":true,"landing_page_url":"http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/95733","pdf_url":null,"source":{"id":"https://openalex.org/S4306400432","display_name":"Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114090438","host_organization_name":"Goethe University Frankfurt","host_organization_lineage":["https://openalex.org/I114090438"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doc-type:preprint"},{"id":"pmh:oai:publikationen.ub.uni-frankfurt.de:95735","is_oa":true,"landing_page_url":"http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/95735","pdf_url":null,"source":{"id":"https://openalex.org/S4306400432","display_name":"Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114090438","host_organization_name":"Goethe University Frankfurt","host_organization_lineage":["https://openalex.org/I114090438"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doc-type:preprint"},{"id":"doi:10.48550/arxiv.2409.03646","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2409.03646","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2409.03646","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.03646","pdf_url":"https://arxiv.org/pdf/2409.03646","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1426318481","display_name":null,"funder_award_id":"grant","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G2300736770","display_name":null,"funder_award_id":"(CSC)","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G4132985236","display_name":null,"funder_award_id":"unknown","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G429533842","display_name":null,"funder_award_id":"CI241/3-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6052429835","display_name":null,"funder_award_id":"(DFG)","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6058657651","display_name":null,"funder_award_id":"CI241/7-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6440785352","display_name":null,"funder_award_id":"CI241/1-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6453091826","display_name":null,"funder_award_id":"PhD stipend","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G7284261321","display_name":null,"funder_award_id":"41/7-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G8589651859","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320324521","display_name":"Freie Universit\u00e4t Berlin","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403556737.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W3211393740","https://openalex.org/W3208049411","https://openalex.org/W3022908591","https://openalex.org/W4285706568"],"abstract_inverted_index":{"In":[0,76],"contrast":[1],"to":[2,12,23,30,38,85,89,98,122,203],"human":[3,28,86,190],"vision,":[4],"artificial":[5],"neural":[6],"networks":[7],"(ANNs)":[8],"remain":[9],"relatively":[10],"susceptible":[11],"adversarial":[13,123,147],"attacks.":[14,124],"To":[15],"address":[16],"this":[17,77],"vulnerability,":[18],"efforts":[19,200],"have":[20],"been":[21],"made":[22],"transfer":[24],"inductive":[25],"bias":[26],"from":[27,57,172,180],"brains":[29],"ANNs,":[31],"often":[32,135],"by":[33],"training":[34],"the":[35,61,130,170,183,211],"ANN":[36],"representations":[37,84],"match":[39],"their":[40,116,144],"biological":[41],"counterparts.":[42],"Previous":[43],"works":[44],"relied":[45],"on":[46,105],"brain":[47],"data":[48,171],"acquired":[49],"in":[50,146,182],"rodents":[51],"or":[52],"primates":[53],"using":[54],"invasive":[55],"techniques,":[56],"specific":[58],"regions":[59],"of":[60,93,109,189,213],"brain,":[62],"under":[63,206],"non-natural":[64],"conditions":[65,209],"(anesthetized":[66],"animals),":[67],"and":[68,74,111,114,120,143,161,176],"with":[69,210],"stimulus":[70,141],"datasets":[71,205],"lacking":[72],"diversity":[73],"naturalness.":[75],"work,":[78],"we":[79,101],"explored":[80],"whether":[81],"aligning":[82],"model":[83],"EEG":[87,112,117,132,174,191],"responses":[88],"a":[90,106],"rich":[91],"set":[92],"real-world":[94],"images":[95],"increases":[96],"robustness":[97,121],"ANNs.":[99],"Specifically,":[100],"trained":[102],"ResNet50-backbone":[103],"models":[104],"dual":[107],"task":[108],"classification":[110],"prediction;":[113],"evaluated":[115],"prediction":[118,133],"accuracy":[119],"We":[125,166],"observed":[126,177],"significant":[127],"correlation":[128],"between":[129],"networks'":[131],"accuracy,":[134],"highest":[136],"around":[137],"100":[138],"ms":[139],"post":[140],"onset,":[142],"gains":[145],"robustness.":[148],"Although":[149],"effect":[150],"size":[151],"was":[152],"limited,":[153],"effects":[154],"were":[155],"consistent":[156],"across":[157],"different":[158],"random":[159],"initializations":[160],"robust":[162],"for":[163,192,198],"architectural":[164],"variants.":[165],"further":[167],"teased":[168],"apart":[169],"individual":[173],"channels":[175],"strongest":[178],"contribution":[179],"electrodes":[181],"parieto-occipital":[184],"regions.":[185],"The":[186],"demonstrated":[187],"utility":[188],"such":[193],"tasks":[194],"opens":[195],"up":[196],"avenues":[197],"future":[199],"that":[201],"scale":[202],"larger":[204],"diverse":[207],"stimuli":[208],"promise":[212],"stronger":[214],"effects.":[215]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
