{"id":"https://openalex.org/W7154254703","doi":"https://doi.org/10.48550/arxiv.2604.09657","title":"Prints in the Magnetic Dust: Robust Similarity Search in Legacy Media Images Using Checksum Count Vectors","display_name":"Prints in the Magnetic Dust: Robust Similarity Search in Legacy Media Images Using Checksum Count Vectors","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7154254703","doi":"https://doi.org/10.48550/arxiv.2604.09657"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.09657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.09657","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092491308","display_name":"Maciej Grzeszczuk","orcid":"https://orcid.org/0000-0002-9840-3398"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Grzeszczuk, Maciej","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091583320","display_name":"Kinga Skorupska","orcid":"https://orcid.org/0000-0002-9005-0348"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Skorupska, Kinga","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133557040","display_name":"Grzegorz M. W\u00f3jcik","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"W\u00f3jcik, Grzegorz M.","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/T12357","display_name":"Digital Media Forensic Detection","score":0.27129998803138733,"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"}},"topics":[{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":0.27129998803138733,"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/T11657","display_name":"Digital and Traditional Archives Management","score":0.15330000221729279,"subfield":{"id":"https://openalex.org/subfields/1206","display_name":"Conservation"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.07169999927282333,"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/checksum","display_name":"Checksum","score":0.8242999911308289},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5651999711990356},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5561000108718872},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5365999937057495},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.43869999051094055},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4050999879837036},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.3734999895095825}],"concepts":[{"id":"https://openalex.org/C162372511","wikidata":"https://www.wikidata.org/wiki/Q218341","display_name":"Checksum","level":2,"score":0.8242999911308289},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7386999726295471},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5651999711990356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5580000281333923},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5561000108718872},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5365999937057495},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.43869999051094055},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4300000071525574},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4050999879837036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3871000111103058},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3734999895095825},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C2776140076","wikidata":"https://www.wikidata.org/wiki/Q193663","display_name":"Magnetic tape","level":3,"score":0.32760000228881836},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32739999890327454},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C42781572","wikidata":"https://www.wikidata.org/wiki/Q1250322","display_name":"Digital image","level":4,"score":0.2989000082015991},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C2776674983","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image editing","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.09657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2604.09657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.09657","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"display_name":"Sustainable cities and communities","score":0.7006956338882446,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Digitizing":[0],"magnetic":[1],"media":[2],"containing":[3],"computer":[4],"data":[5,83],"is":[6],"only":[7],"the":[8,12],"first":[9],"step":[10,125],"towards":[11,126],"preservation":[13],"of":[14,36,78,92,117,136],"early":[15],"home":[16],"computing":[17],"era":[18],"artifacts.":[19],"The":[20,85],"audio":[21],"tape":[22,94],"images":[23,95],"must":[24],"be":[25,40],"decoded,":[26],"verified,":[27],"repaired":[28],"if":[29],"necessary,":[30],"tested,":[31],"and":[32,49,69,76,103,133,145],"documented.":[33],"If":[34],"parts":[35],"this":[37],"process":[38],"could":[39,44],"effectively":[41],"automated,":[42],"volunteers":[43],"focus":[45],"on":[46,65,89],"contributing":[47],"contextual":[48],"historical":[50,137],"knowledge":[51,146],"rather":[52],"than":[53],"struggling":[54],"with":[55,113],"technical":[56],"tools.":[57],"We":[58],"therefore":[59],"propose":[60],"a":[61,81,90],"feature":[62],"representation":[63],"based":[64],"Checksum":[66],"Count":[67],"Vectors":[68],"evaluate":[70],"its":[71],"applicability":[72],"to":[73,115],"detecting":[74,101],"duplicates":[75],"variants":[77,102],"recordings":[79,112],"within":[80],"large":[82],"store.":[84],"approach":[86],"was":[87],"tested":[88],"collection":[91],"decoded":[93],"(n=4902),":[96],"achieving":[97],"58\\%":[98],"accuracy":[99,105],"in":[100,106],"97%":[104],"identifying":[107],"alternative":[108],"copies,":[109],"for":[110,130],"damaged":[111],"up":[114],"75%":[116],"records":[118],"missing.":[119],"These":[120],"results":[121],"represent":[122],"an":[123],"important":[124],"fully":[127],"automated":[128],"pipelines":[129],"restoration,":[131],"de-duplication,":[132],"semantic":[134],"integration":[135],"digital":[138],"artifacts":[139],"through":[140],"sequence":[141],"matching,":[142],"automatic":[143],"repair":[144],"discovery.":[147]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-15T00:00:00"}