{"id":"https://openalex.org/W7130716822","doi":"https://doi.org/10.48550/arxiv.2602.17322","title":"Leveraging Contrastive Learning for a Similarity-Guided Tampered Document Data Generation Pipeline","display_name":"Leveraging Contrastive Learning for a Similarity-Guided Tampered Document Data Generation Pipeline","publication_year":2026,"publication_date":"2026-02-19","ids":{"openalex":"https://openalex.org/W7130716822","doi":"https://doi.org/10.48550/arxiv.2602.17322"},"language":"en","primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.17322","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091248733","display_name":"Mohamed Dhouib","orcid":"https://orcid.org/0000-0002-5587-1028"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dhouib, Mohamed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051885442","display_name":"Davide Buscaldi","orcid":"https://orcid.org/0000-0003-1112-3789"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Buscaldi, Davide","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114420300","display_name":"Sonia Vanier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vanier, Sonia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5052180718","display_name":"Aymen Shabou","orcid":"https://orcid.org/0000-0001-8933-7053"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shabou, Aymen","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22235404,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.5877000093460083,"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":0.5877000093460083,"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":0.1446000039577484,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.11069999635219574,"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/pipeline","display_name":"Pipeline (software)","score":0.9121000170707703},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.6001999974250793},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5655999779701233},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5012999773025513},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.383899986743927},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.29510000348091125},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.28700000047683716}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.9121000170707703},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8528000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.612500011920929},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.6001999974250793},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5655999779701233},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5012999773025513},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4975000023841858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44209998846054077},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3359000086784363},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32030001282691956},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.29510000348091125},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C2780479914","wikidata":"https://www.wikidata.org/wiki/Q302088","display_name":"Document classification","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.17322","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:HAL:hal-05531450v1","is_oa":false,"landing_page_url":"https://hal.science/hal-05531450","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2026","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2602.17322","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.17322","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.17322","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"No poverty","score":0.4692654013633728,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Detecting":[0],"tampered":[1,23,83,159],"text":[2,94],"in":[3,50,65],"document":[4,84,160],"images":[5],"is":[6],"a":[7,77,100,114,121,141,152],"challenging":[8],"task":[9],"due":[10],"to":[11,21,58,92,118],"data":[12,168],"scarcity.":[13],"To":[14],"address":[15],"this,":[16],"previous":[17],"work":[18],"has":[19],"attempted":[20],"generate":[22],"documents":[24,31],"using":[25,184],"rule-based":[26],"methods.":[27],"However,":[28],"the":[29,55,125,164,179],"resulting":[30],"often":[32],"suffer":[33],"from":[34,178],"limited":[35],"variety":[36],"and":[37,63,107,187,211],"poor":[38,66],"visual":[39],"quality,":[40],"typically":[41],"leaving":[42],"highly":[43],"visible":[44],"artifacts":[45],"that":[46,146,202],"are":[47],"rarely":[48],"observed":[49],"real-world":[51,69],"manipulations.":[52],"This":[53],"undermines":[54],"model's":[56],"ability":[57],"learn":[59],"robust,":[60],"generalizable":[61],"features":[62],"results":[64],"performance":[67,207],"on":[68,175,197],"data.":[70],"Motivated":[71],"by":[72,171],"this":[73],"discrepancy,":[74],"we":[75,150],"propose":[76],"novel":[78,101],"method":[79,186],"for":[80,103],"generating":[81],"high-quality":[82,158],"images.":[85,161],"We":[86,111,162],"first":[87],"train":[88,113],"an":[89],"auxiliary":[90,116],"network":[91,117],"compare":[93],"crops,":[95],"leveraging":[96],"contrastive":[97],"learning":[98],"with":[99],"strategy":[102],"defining":[104],"positive":[105],"pairs":[106],"their":[108],"corresponding":[109],"negatives.":[110],"also":[112],"second":[115],"evaluate":[119],"whether":[120],"crop":[122],"tightly":[123],"encloses":[124],"intended":[126],"characters,":[127],"without":[128],"cutting":[129],"off":[130],"parts":[131,136],"of":[132,137,155,166],"characters":[133],"or":[134],"including":[135],"adjacent":[138],"ones.":[139],"Using":[140],"carefully":[142],"designed":[143],"generation":[144,169],"pipeline":[145,170,204],"leverages":[147],"both":[148],"networks,":[149],"introduce":[151],"framework":[153],"capable":[154],"producing":[156],"diverse,":[157],"assess":[163],"effectiveness":[165],"our":[167,185,203],"training":[172,192],"multiple":[173],"models":[174,196],"datasets":[176,200],"derived":[177],"same":[180],"source":[181],"images,":[182],"generated":[183],"existing":[188],"approaches,":[189],"under":[190],"identical":[191],"protocols.":[193],"Evaluating":[194],"these":[195],"various":[198],"open-source":[199],"shows":[201],"yields":[205],"consistent":[206],"improvements":[208],"across":[209],"architectures":[210],"datasets.":[212]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-02-21T00:00:00"}