{"id":"https://openalex.org/W7147198395","doi":"https://doi.org/10.48550/arxiv.2603.28824","title":"SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation","display_name":"SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation","publication_year":2026,"publication_date":"2026-03-29","ids":{"openalex":"https://openalex.org/W7147198395","doi":"https://doi.org/10.48550/arxiv.2603.28824"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.28824","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28824","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.28824","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132661047","display_name":"He Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132681659","display_name":"Dongyi Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lv, Dongyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132591179","display_name":"Song Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Song","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132620314","display_name":"Wei Xi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xi, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132600497","display_name":"Jizhong Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Jizhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.9796000123023987,"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":0.9796000123023987,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0017999999690800905,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0015999999595806003,"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/backdoor","display_name":"Backdoor","score":0.9646000266075134},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6643999814987183},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49889999628067017},{"id":"https://openalex.org/keywords/invisibility","display_name":"Invisibility","score":0.4731000065803528},{"id":"https://openalex.org/keywords/vulnerability","display_name":"Vulnerability (computing)","score":0.4546000063419342},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4327000081539154},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3806000053882599},{"id":"https://openalex.org/keywords/condensation","display_name":"Condensation","score":0.35040000081062317}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.9646000266075134},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6643999814987183},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6263999938964844},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49889999628067017},{"id":"https://openalex.org/C50962388","wikidata":"https://www.wikidata.org/wiki/Q762018","display_name":"Invisibility","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.4546000063419342},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4293000102043152},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3806000053882599},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3517000079154968},{"id":"https://openalex.org/C200093464","wikidata":"https://www.wikidata.org/wiki/Q166583","display_name":"Condensation","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3255999982357025},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.32499998807907104},{"id":"https://openalex.org/C2780841128","wikidata":"https://www.wikidata.org/wiki/Q5073781","display_name":"Characterization (materials science)","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.313400000333786},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C2776777543","wikidata":"https://www.wikidata.org/wiki/Q1361182","display_name":"Air traffic management","level":3,"score":0.298799991607666},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28940001130104065},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.2597000002861023}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.28824","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28824","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.28824","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28824","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Dataset":[0],"condensation":[1,27,42],"aims":[2],"to":[3,32,132,135],"synthesize":[4],"compact":[5],"yet":[6],"informative":[7],"datasets":[8,146],"that":[9,25,114,148],"retain":[10],"the":[11,26,41,74,81,102,130,165,169],"training":[12],"efficacy":[13],"of":[14,77,105,167],"full-scale":[15],"data,":[16],"offering":[17],"substantial":[18],"gains":[19],"in":[20,55,68,72],"efficiency.":[21],"Recent":[22],"studies":[23],"reveal":[24],"process":[28],"can":[29],"be":[30],"vulnerable":[31],"backdoor":[33],"attacks,":[34],"where":[35],"malicious":[36],"triggers":[37,117],"are":[38],"injected":[39],"into":[40],"dataset,":[43],"manipulating":[44],"model":[45],"behavior":[46],"during":[47,84],"inference.":[48,85],"While":[49],"prior":[50],"approaches":[51],"have":[52],"made":[53],"progress":[54],"balancing":[56],"attack":[57,98,131,155,178],"success":[58,156],"rate":[59],"and":[60,109,139,161,172],"clean":[61,158],"test":[62,159],"accuracy,":[63,160],"they":[64],"often":[65],"fall":[66],"short":[67],"preserving":[69],"stealthiness,":[70,162],"especially":[71],"concealing":[73],"visual":[75],"artifacts":[76],"condensed":[78],"data":[79,171],"or":[80],"perturbations":[82],"introduced":[83],"To":[86],"address":[87],"this":[88],"challenge,":[89],"we":[90],"introduce":[91],"Sneakdoor,":[92],"which":[93],"enhances":[94],"stealthiness":[95],"without":[96],"compromising":[97],"effectiveness.":[99],"Sneakdoor":[100,149],"exploits":[101],"inherent":[103],"vulnerability":[104],"class":[106],"decision":[107],"boundaries":[108],"incorporates":[110],"a":[111,151],"generative":[112],"module":[113],"constructs":[115],"input-aware":[116],"aligned":[118],"with":[119],"local":[120],"feature":[121],"geometry,":[122],"thereby":[123],"minimizing":[124],"detectability.":[125],"This":[126],"joint":[127],"design":[128],"enables":[129],"remain":[133],"imperceptible":[134],"both":[136,168],"human":[137],"inspection":[138],"statistical":[140],"detection.":[141],"Extensive":[142],"experiments":[143],"across":[144],"multiple":[145],"demonstrate":[147],"achieves":[150],"compelling":[152],"balance":[153],"among":[154],"rate,":[157],"substantially":[163],"improving":[164],"invisibility":[166],"synthetic":[170],"triggered":[173],"samples":[174],"while":[175],"maintaining":[176],"high":[177],"efficacy.":[179],"The":[180],"code":[181],"is":[182],"available":[183],"at":[184],"https://github.com/XJTU-AI-Lab/SneakDoor.":[185]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
