{"id":"https://openalex.org/W4403791880","doi":"https://doi.org/10.1145/3664647.3680968","title":"Learning from Distinction: Mitigating Backdoors Using a Low-Capacity Model","display_name":"Learning from Distinction: Mitigating Backdoors Using a Low-Capacity Model","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791880","doi":"https://doi.org/10.1145/3664647.3680968"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3680968","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680968","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","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/A5103466697","display_name":"Haohui Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haosen Sun","raw_affiliation_strings":["School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0006-3995-5156","affiliations":[{"raw_affiliation_string":"School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064725095","display_name":"Yiming Li","orcid":"https://orcid.org/0009-0006-2003-4760"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Li","raw_affiliation_strings":["School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0007-6782-1339","affiliations":[{"raw_affiliation_string":"School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059296628","display_name":"Xixiang Lv","orcid":"https://orcid.org/0000-0003-2879-241X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xixiang Lyu","raw_affiliation_strings":["School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0003-2879-241X","affiliations":[{"raw_affiliation_string":"School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040179664","display_name":"Jing Ma","orcid":"https://orcid.org/0000-0002-5217-3910"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Ma","raw_affiliation_strings":["School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0002-5217-3910","affiliations":[{"raw_affiliation_string":"School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103466697"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16337759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"9077","last_page":"9086"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9997000098228455,"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":0.9997000098228455,"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":0.996399998664856,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9887999892234802,"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/computer-science","display_name":"Computer science","score":0.6305276155471802},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.4162295162677765}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6305276155471802},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4162295162677765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3680968","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3680968","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W569478347","https://openalex.org/W2046532797","https://openalex.org/W2108598243","https://openalex.org/W2112796928","https://openalex.org/W2117539524","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2543927648","https://openalex.org/W2807363941","https://openalex.org/W2934843808","https://openalex.org/W2963163009","https://openalex.org/W2970335439","https://openalex.org/W2986013765","https://openalex.org/W2990270730","https://openalex.org/W2990605346","https://openalex.org/W2997076693","https://openalex.org/W3012113073","https://openalex.org/W3034258347","https://openalex.org/W3034414373","https://openalex.org/W3034579202","https://openalex.org/W3042368254","https://openalex.org/W3043789969","https://openalex.org/W3048759177","https://openalex.org/W3083045783","https://openalex.org/W3083185154","https://openalex.org/W3093239278","https://openalex.org/W3107337211","https://openalex.org/W3111943226","https://openalex.org/W3117572899","https://openalex.org/W3121099749","https://openalex.org/W3152758407","https://openalex.org/W3209100754","https://openalex.org/W3210227505","https://openalex.org/W3212023986","https://openalex.org/W3214302844","https://openalex.org/W3214636874","https://openalex.org/W4214737857","https://openalex.org/W4230172274","https://openalex.org/W4312329503","https://openalex.org/W4312406341","https://openalex.org/W4312536478","https://openalex.org/W4313192591","https://openalex.org/W4320002812","https://openalex.org/W4375869280","https://openalex.org/W4386076050","https://openalex.org/W6796340106"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Deep":[0],"neural":[1],"networks":[2],"(DNNs)":[3],"are":[4,29],"susceptible":[5],"to":[6,10,21,96,126],"backdoor":[7,41,54,71],"attacks":[8,19,55],"due":[9],"their":[11],"black-box":[12],"nature":[13],"and":[14,43,115,129,142],"lack":[15],"of":[16,100],"interpretability.":[17],"Backdoor":[18],"intend":[20],"manipulate":[22],"the":[23,46,57,85,98],"model's":[24],"prediction":[25],"when":[26],"hidden":[27],"backdoors":[28],"activated":[30],"by":[31],"predefined":[32],"triggers.":[33],"Although":[34],"considerable":[35],"progress":[36],"has":[37],"been":[38],"made":[39],"in":[40],"detection":[42],"removal":[44],"at":[45],"model":[47,83,92,104],"deployment":[48],"stage,":[49],"an":[50],"effective":[51],"defense":[52,72],"against":[53],"during":[56],"training":[58,80],"time":[59],"is":[60],"still":[61],"under-explored.":[62],"In":[63],"this":[64],"paper,":[65],"we":[66],"propose":[67],"a":[68,81,90,94,101,106],"novel":[69],"training-time":[70],"method":[73],"called":[74],"Learning":[75],"from":[76],"Distinction":[77],"(LfD),":[78],"allowing":[79],"backdoor-free":[82,102],"on":[84,112,135],"backdoor-poisoned":[86],"data.":[87],"LfD":[88,120],"uses":[89],"low-capacity":[91],"as":[93],"teacher":[95],"guide":[97],"learning":[99],"student":[103],"via":[105],"dynamic":[107],"weighting":[108],"strategy.":[109],"Extensive":[110],"experiments":[111],"CIFAR-10,":[113],"GTSRB":[114],"ImageNet-subset":[116],"datasets":[117],"show":[118],"that":[119],"significantly":[121],"reduces":[122],"attack":[123],"success":[124],"rates":[125],"0.67%,":[127],"6.14%":[128],"1.42%,":[130],"respectively,":[131],"with":[132],"minimal":[133],"impact":[134],"clean":[136],"accuracy":[137],"(less":[138],"than":[139],"1%,":[140],"3%":[141],"1%).":[143]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}