{"id":"https://openalex.org/W7127328050","doi":"https://doi.org/10.1109/access.2026.3660716","title":"Lateralized Learning for Multi-Class Visual Classification Tasks","display_name":"Lateralized Learning for Multi-Class Visual Classification Tasks","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7127328050","doi":"https://doi.org/10.1109/access.2026.3660716"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3660716","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660716","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3660716","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124911862","display_name":"Abubakar Siddique","orcid":null},"institutions":[{"id":"https://openalex.org/I39854758","display_name":"Auckland University of Technology","ror":"https://ror.org/01zvqw119","country_code":"NZ","type":"education","lineage":["https://openalex.org/I39854758"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Abubakar Siddique","raw_affiliation_strings":["School of Engineering and Computer Science, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand"],"raw_orcid":"https://orcid.org/0000-0002-3253-802X","affiliations":[{"raw_affiliation_string":"School of Engineering and Computer Science, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand","institution_ids":["https://openalex.org/I39854758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112664235","display_name":"Prof Will Browne","orcid":null},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Will N. Browne","raw_affiliation_strings":["Faculty of Engineering, School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD, Australia"],"raw_orcid":"https://orcid.org/0000-0001-8979-2224","affiliations":[{"raw_affiliation_string":"Faculty of Engineering, School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD, Australia","institution_ids":["https://openalex.org/I160993911"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071797847","display_name":"Gina M. Grimshaw","orcid":"https://orcid.org/0000-0002-1291-1368"},"institutions":[{"id":"https://openalex.org/I41156924","display_name":"Victoria University of Wellington","ror":"https://ror.org/0040r6f76","country_code":"NZ","type":"education","lineage":["https://openalex.org/I41156924"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Gina M. Grimshaw","raw_affiliation_strings":["Cognitive and Affective Neuroscience Laboratory, School of Psychology, Victoria University of Wellington, Wellington, New Zealand"],"raw_orcid":"https://orcid.org/0000-0002-1291-1368","affiliations":[{"raw_affiliation_string":"Cognitive and Affective Neuroscience Laboratory, School of Psychology, Victoria University of Wellington, Wellington, New Zealand","institution_ids":["https://openalex.org/I41156924"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5124911862"],"corresponding_institution_ids":["https://openalex.org/I39854758"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19815842,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"19128","last_page":"19142"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9937000274658203,"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":0.9937000274658203,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.000699999975040555,"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"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.00039999998989515007,"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/adversarial-system","display_name":"Adversarial system","score":0.7657999992370605},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5906999707221985},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5030999779701233},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5006999969482422},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.436599999666214},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4242999851703644},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4027000069618225},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.35910001397132874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8027999997138977},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7657999992370605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6718999743461609},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5906999707221985},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5030999779701233},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5006999969482422},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4431999921798706},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35910001397132874},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.35120001435279846},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.32600000500679016},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.30250000953674316},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2930000126361847},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.2736000120639801},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.25839999318122864}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2026.3660716","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660716","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:openrepository.aut.ac.nz:10292/20642","is_oa":true,"landing_page_url":"http://hdl.handle.net/10292/20642","pdf_url":"https://openrepository.aut.ac.nz/bitstreams/b3ab71b7-a87a-4c7c-8076-1e34a4c43fd9/download","source":{"id":"https://openalex.org/S4306401809","display_name":"Tuwhera (Auckland University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39854758","host_organization_name":"Auckland University of Technology","host_organization_lineage":["https://openalex.org/I39854758"],"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":"Journal Article"},{"id":"pmh:oai:doaj.org/article:769b6a01ba6c4acea5a46eb4f6b1bfc8","is_oa":false,"landing_page_url":"https://doaj.org/article/769b6a01ba6c4acea5a46eb4f6b1bfc8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 19128-19142 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3660716","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660716","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W1536113745","https://openalex.org/W1785570092","https://openalex.org/W1925349544","https://openalex.org/W1972013500","https://openalex.org/W1990690868","https://openalex.org/W2002076628","https://openalex.org/W2023131636","https://openalex.org/W2033245841","https://openalex.org/W2034033026","https://openalex.org/W2041308297","https://openalex.org/W2063691920","https://openalex.org/W2076137887","https://openalex.org/W2076201615","https://openalex.org/W2094999461","https://openalex.org/W2100805904","https://openalex.org/W2113242816","https://openalex.org/W2116930929","https://openalex.org/W2120536063","https://openalex.org/W2124386111","https://openalex.org/W2133794799","https://openalex.org/W2145624268","https://openalex.org/W2146712365","https://openalex.org/W2151103935","https://openalex.org/W2155806188","https://openalex.org/W2161969291","https://openalex.org/W2167917621","https://openalex.org/W2168847089","https://openalex.org/W2192582052","https://openalex.org/W2194775991","https://openalex.org/W2243397390","https://openalex.org/W2434184590","https://openalex.org/W2498672755","https://openalex.org/W2543927648","https://openalex.org/W2556590024","https://openalex.org/W2557422176","https://openalex.org/W2605427989","https://openalex.org/W2618530766","https://openalex.org/W2625755437","https://openalex.org/W2765424254","https://openalex.org/W2921745792","https://openalex.org/W2926786214","https://openalex.org/W2962700793","https://openalex.org/W2962761044","https://openalex.org/W2963542245","https://openalex.org/W2966576392","https://openalex.org/W2979544394","https://openalex.org/W2995523160","https://openalex.org/W3021182048","https://openalex.org/W3038421772","https://openalex.org/W3046519091","https://openalex.org/W3112181535","https://openalex.org/W3119923413","https://openalex.org/W3127158680","https://openalex.org/W3142406396","https://openalex.org/W3192570781","https://openalex.org/W3196059712","https://openalex.org/W3211008687","https://openalex.org/W4225096539","https://openalex.org/W4237402488","https://openalex.org/W4242037245","https://openalex.org/W4253365321","https://openalex.org/W4285153705","https://openalex.org/W4285226149","https://openalex.org/W4301007422","https://openalex.org/W4312623428","https://openalex.org/W4385627129","https://openalex.org/W4391019749","https://openalex.org/W4391342316","https://openalex.org/W4394670654","https://openalex.org/W4400762160","https://openalex.org/W4406188235","https://openalex.org/W4406237488"],"related_works":[],"abstract_inverted_index":{"The":[0,157],"majority":[1],"of":[2,37,107,119,193],"computer":[3],"vision":[4,86],"algorithms":[5],"fail":[6],"to":[7,57,64,90,112,123,152],"find":[8],"higher-order":[9,47],"(abstract)":[10],"patterns":[11,48],"in":[12,29],"an":[13],"image":[14,164],"so":[15],"they":[16],"are":[17,42,49,55,150],"not":[18,44,50],"robust":[19],"against":[20],"adversarial":[21,91,138,196],"attacks.":[22,92],"Deep":[23],"learning":[24,103],"considers":[25],"each":[26],"input":[27],"pixel":[28],"a":[30,38,120],"homogeneous":[31],"manner":[32],"such":[33],"that":[34,79,126,168],"different":[35,105],"parts":[36],"locality-sensitive":[39],"hashing":[40],"table":[41],"often":[43],"connected,":[45],"meaning":[46],"discovered.":[51],"Hence,":[52],"these":[53],"systems":[54,189],"sensitive":[56],"noisy,":[58,128],"irrelevant,":[59,129],"and":[60,117,130,135,145,195,202],"redundant":[61,131],"data,":[62],"leading":[63],"wrong":[65],"predictions":[66],"with":[67],"high":[68],"confidence.":[69],"Adversarial":[70,148],"attacks":[71],"exploit":[72],"this":[73],"vulnerability":[74],"by":[75,198],"generating":[76],"deceptive":[77],"inputs":[78],"mislead":[80],"AI":[81],"systems.":[82],"In":[83],"contrast,":[84],"human":[85],"is":[87],"rarely":[88],"susceptible":[89],"Vertebrate":[93],"brains":[94],"afford":[95],"heterogeneous":[96],"knowledge":[97,175],"representation":[98],"through":[99],"lateralization,":[100],"enabling":[101],"modular":[102],"at":[104],"levels":[106],"abstraction.":[108],"This":[109],"work":[110],"aims":[111],"verify":[113],"the":[114,140,146,169,181,191],"effectiveness,":[115],"scalability,":[116],"robustness":[118],"lateralized":[121,182],"approach":[122],"real-world":[124],"problems":[125],"contain":[127],"data.":[132],"Two":[133],"well-known":[134],"widely":[136],"used":[137],"attacks,":[139],"Fast":[141],"Gradient":[142],"Sign":[143],"Method":[144],"Iterative":[147],"Technique,":[149],"applied":[151],"generate":[153],"corrupted":[154],"test":[155],"images.":[156],"experimental":[158],"results":[159],"on":[160],"multi-class":[161],"(200":[162],"classes)":[163],"classification":[165,192],"tasks":[166],"demonstrate":[167],"proposed":[170],"system":[171,183],"effectively":[172],"captures":[173],"hierarchical":[174],"representations,":[176],"enhancing":[177],"its":[178],"robustness.":[179],"Crucially,":[180],"outperformed":[184],"four":[185],"state-of-the-art":[186],"deep":[187],"learning-based":[188],"for":[190],"normal":[194],"images":[197],"19.05%":[199],"\u2212":[200,204],"41.02%":[201],"1.36%":[203],"49.22%,":[205],"respectively.":[206]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-02-04T00:00:00"}
