{"id":"https://openalex.org/W4415316365","doi":"https://doi.org/10.48550/arxiv.2509.12633","title":"CIARD: Cyclic Iterative Adversarial Robustness Distillation","display_name":"CIARD: Cyclic Iterative Adversarial Robustness Distillation","publication_year":2025,"publication_date":"2025-09-16","ids":{"openalex":"https://openalex.org/W4415316365","doi":"https://doi.org/10.48550/arxiv.2509.12633"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.12633","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.12633","pdf_url":"https://arxiv.org/pdf/2509.12633","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.12633","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069103616","display_name":"Liming Lu","orcid":"https://orcid.org/0000-0001-7821-4699"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lu, Liming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088461583","display_name":"Shuchao Pang","orcid":"https://orcid.org/0000-0002-5668-833X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Shuchao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101651954","display_name":"Zheng Xu","orcid":"https://orcid.org/0000-0003-1932-1026"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Xu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114227689","display_name":"Xiang Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Xiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108270107","display_name":"Anan Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Anan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082653046","display_name":"Yunhuai Liu","orcid":"https://orcid.org/0000-0002-1180-8078"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yunhuai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5063991302","display_name":"Yongbin Zhou","orcid":"https://orcid.org/0000-0002-1337-0655"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yongbin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5069103616"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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":0.9955999851226807,"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":0.9955999851226807,"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":0.9726999998092651,"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"}},{"id":"https://openalex.org/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.9556999802589417,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.8774999976158142},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.847599983215332},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.4178999960422516},{"id":"https://openalex.org/keywords/robust-optimization","display_name":"Robust optimization","score":0.36500000953674316},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3172999918460846},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.3066999912261963}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8774999976158142},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.847599983215332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7009000182151794},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4345000088214874},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3831999897956848},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3458000123500824},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C2778445095","wikidata":"https://www.wikidata.org/wiki/Q18354077","display_name":"Sample complexity","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.12633","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.12633","pdf_url":"https://arxiv.org/pdf/2509.12633","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.12633","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.12633","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.12633","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.12633","pdf_url":"https://arxiv.org/pdf/2509.12633","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"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":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Adversarial":[0],"robustness":[1,10,139,189],"distillation":[2],"(ARD)":[3],"aims":[4],"to":[5,14,36,76,90,123,135],"transfer":[6,75],"both":[7],"performance":[8,20,39,91,141,160],"and":[9,69,80,130,153,174,190],"from":[11,143],"teacher":[12,96,138],"model":[13,188],"lightweight":[15],"student":[16,29,78],"model,":[17,79],"enabling":[18],"resilient":[19],"on":[21,40,150],"resource-constrained":[22],"scenarios.":[23],"Though":[24],"existing":[25,52],"ARD":[26,108],"approaches":[27],"enhance":[28],"model's":[30],"robustness,":[31],"the":[32,37,45,67,77,94,144],"inevitable":[33],"by-product":[34],"leads":[35],"degraded":[38],"clean":[41,68,179],"examples.":[42,147],"We":[43],"summarize":[44],"causes":[46],"of":[47,63,93],"this":[48],"problem":[49],"inherent":[50],"in":[51,126,166,178],"methods":[53],"with":[54,111,119,161],"dual-teacher":[55,64,127],"framework":[56,118],"as:":[57],"1.":[58],"The":[59,82],"divergent":[60],"optimization":[61,128],"objectives":[62],"models,":[65],"i.e.,":[66],"robust":[70,95],"teachers,":[71],"impede":[72],"effective":[73],"knowledge":[74],"2.":[81],"iteratively":[83],"generated":[84],"adversarial":[85,133,146,167],"examples":[86],"during":[87],"training":[88],"lead":[89],"deterioration":[92],"model.":[97],"To":[98],"address":[99],"these":[100],"challenges,":[101],"we":[102],"propose":[103],"a":[104,175,183],"novel":[105],"Cyclic":[106],"Iterative":[107],"(CIARD)":[109],"method":[110],"two":[112],"key":[113],"innovations:":[114],"a.":[115],"A":[116],"multi-teacher":[117],"contrastive":[120],"push-loss":[121],"alignment":[122],"resolve":[124],"conflicts":[125],"objectives,":[129],"b.":[131],"Continuous":[132],"retraining":[134],"maintain":[136],"dynamic":[137],"against":[140],"degradation":[142],"varying":[145],"Extensive":[148],"experiments":[149],"CIFAR-10,":[151],"CIFAR-100,":[152],"Tiny-ImageNet":[154],"demonstrate":[155],"that":[156],"CIARD":[157],"achieves":[158],"remarkable":[159],"an":[162],"average":[163],"3.53":[164],"improvement":[165],"defense":[168],"rates":[169],"across":[170],"various":[171],"attack":[172],"scenarios":[173],"5.87":[176],"increase":[177],"sample":[180],"accuracy,":[181],"establishing":[182],"new":[184],"benchmark":[185],"for":[186],"balancing":[187],"generalization.":[191],"Our":[192],"code":[193],"is":[194],"available":[195],"at":[196],"https://github.com/eminentgu/CIARD":[197]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-18T00:00:00"}