{"id":"https://openalex.org/W7165685163","doi":"https://doi.org/10.48550/arxiv.2606.21240","title":"DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration","display_name":"DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration","publication_year":2026,"publication_date":"2026-06-19","ids":{"openalex":"https://openalex.org/W7165685163","doi":"https://doi.org/10.48550/arxiv.2606.21240"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.21240","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21240","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.2606.21240","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139185061","display_name":"Tian Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Tian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139199504","display_name":"Yan Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139208008","display_name":"Shaofeng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shaofeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100725580","display_name":"Guoxing Chen","orcid":"https://orcid.org/0000-0001-8107-5909"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Guoxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139146402","display_name":"Yuling Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yuling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139185041","display_name":"Zhen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058268359","display_name":"Hao Zhu","orcid":"https://orcid.org/0000-0003-4411-0933"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Haojin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139195095","display_name":"Hao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hao","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":0.9283999800682068,"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":0.9283999800682068,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.0066999997943639755,"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":0.005900000222027302,"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/robustness","display_name":"Robustness (evolution)","score":0.7373999953269958},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.6385999917984009},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4683000147342682},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4652000069618225},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4650000035762787},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.41990000009536743},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.39800000190734863},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3544999957084656}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7373999953269958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6777999997138977},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.6385999917984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5920000076293945},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5307999849319458},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5098999738693237},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4683000147342682},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4652000069618225},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4650000035762787},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C2780560020","wikidata":"https://www.wikidata.org/wiki/Q79719","display_name":"License","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3091999888420105},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.21240","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21240","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.2606.21240","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21240","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":{"Training":[0],"datasets":[1],"have":[2],"tremendous":[3],"proprietary":[4],"value":[5],"and":[6,87,90,146,163,170,184],"are":[7],"vulnerable":[8],"to":[9,25,95,107,132],"unauthorized":[10],"copying.":[11],"Existing":[12],"defenses":[13],"mainly":[14],"focus":[15],"on":[16,100,155,160],"tracking":[17,56],"individual":[18],"data":[19],"points,":[20],"but":[21],"pay":[22],"little":[23],"attention":[24],"the":[26,53,111],"threat":[27],"of":[28,35,55,126],"dataset":[29,82],"regeneration.":[30,98,157],"Through":[31],"a":[32,124,138],"measurement":[33],"study":[34],"public":[36],"tumor":[37],"datasets,":[38,166,169],"we":[39,103],"identify":[40,97],"substantial":[41],"real-world":[42],"partial-dataset":[43],"replication,":[44],"raising":[45],"concerns":[46],"about":[47],"potential":[48],"license":[49],"noncompliance.":[50],"To":[51],"counter":[52],"challenge":[54],"previously":[57],"unknown":[58],"adversarial":[59],"regeneration,":[60],"our":[61,108],"key":[62],"insight":[63],"is":[64,110],"that":[65,67,115,141,175],"regeneration":[66,117],"preserves":[68,72],"model":[69],"utility":[70],"inevitably":[71],"measurable":[73],"signals":[74],"across":[75,123],"multiple":[76],"feature":[77],"scales.":[78],"We":[79,135],"categorize":[80],"these":[81,101,143],"features":[83,89],"into":[84],"sample-,":[85],"set-,":[86],"distribution-level":[88],"design":[91],"four":[92],"similarity":[93,121],"metrics":[94,145],"accurately":[96],"Based":[99],"metrics,":[102],"develop":[104],"DIPBox,":[105],"which":[106],"knowledge":[109],"first":[112],"testing":[113,122],"framework":[114],"tracks":[116],"suspects":[118],"via":[119],"multi-scale":[120,144],"spectrum":[125],"defender":[127],"access":[128],"settings,":[129],"from":[130],"limited":[131],"full":[133],"information.":[134],"further":[136],"provide":[137],"learning-theoretic":[139],"analysis":[140],"justifies":[142],"formalizes":[147],"an":[148],"inherent":[149],"utility--divergence":[150],"trade-off,":[151],"implying":[152],"fundamental":[153],"limits":[154,185],"evasive":[156],"Extensive":[158],"experiments":[159],"16":[161],"vision":[162],"text":[164],"base":[165],"320":[167],"regenerated":[168],"590":[171],"derived":[172],"models":[173],"validate":[174],"DIPBox":[176],"outperforms":[177],"previous":[178],"solutions":[179],"while":[180],"characterizing":[181],"its":[182],"robustness":[183],"under":[186],"three":[187],"adaptive":[188],"attacks.":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}