{"id":"https://openalex.org/W7133352627","doi":"https://doi.org/10.48550/arxiv.2603.01433","title":"DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis","display_name":"DOCFORGE-BENCH: A Comprehensive 0-shot Benchmark for Document Forgery Detection and Analysis","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133352627","doi":"https://doi.org/10.48550/arxiv.2603.01433"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01433","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01433","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126675635","display_name":"Zengqi Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhao, Zengqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035019556","display_name":"Weidi Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Weidi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091302737","display_name":"En Bo Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, En","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127903788","display_name":"Yan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Mo, Jane","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mo, Jane","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045478267","display_name":"Tiannan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tiannan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066011592","display_name":"Yuanqin Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Yuanqin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Zexi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zexi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043620947","display_name":"Yiran Tao","orcid":"https://orcid.org/0009-0006-8489-1569"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao, Yiran","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123008945","display_name":"Simiao Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Simiao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5126675635"],"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":0.821399986743927,"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":0.821399986743927,"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":0.043800000101327896,"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":0.023800000548362732,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7838000059127808},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.49880000948905945},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.43549999594688416},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.4246000051498413},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4036000072956085},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4018000066280365},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.39820000529289246},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37299999594688416}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7838000059127808},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7700999975204468},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5561000108718872},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.49880000948905945},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.44190001487731934},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.39820000529289246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39500001072883606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3799000084400177},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C2779696439","wikidata":"https://www.wikidata.org/wiki/Q7512811","display_name":"Signature (topology)","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.33739998936653137},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.31040000915527344},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.2912999987602234},{"id":"https://openalex.org/C512654426","wikidata":"https://www.wikidata.org/wiki/Q19652","display_name":"Public domain","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.26489999890327454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01433","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":"doi:10.48550/arxiv.2603.01433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01433","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":"article"},"sustainable_development_goals":[{"score":0.5173602104187012,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"},{"score":0.42337730526924133,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,200],"present":[1],"DOCFORGE-BENCH,":[2],"the":[3,57,123,142,161,173,208,227],"first":[4],"unified":[5],"zero-shot":[6],"benchmark":[7],"for":[8,177],"document":[9,25,65,108,189,193,219],"forgery":[10,194],"detection,":[11],"evaluating":[12],"14":[13],"methods":[14,40,80],"across":[15],"eight":[16,205],"datasets":[17,206],"spanning":[18],"text":[19],"tampering,":[20],"receipt":[21],"forgery,":[22],"and":[23,46,217],"identity":[24],"manipulation.":[26],"Unlike":[27],"fine-tuning-oriented":[28],"evaluations":[29],"such":[30],"as":[31],"ForensicHub":[32],"[Du":[33],"et":[34],"al.,":[35],"2025],":[36],"DOCFORGE-BENCH":[37],"applies":[38],"all":[39,204],"with":[41],"their":[42],"published":[43],"pretrained":[44],"weights":[45],"no":[47,181],"domain":[48,156],"adaptation":[49,167],"--":[50,110,121,168,171],"a":[51,72,93,97,151,222],"deliberate":[52],"design":[53],"choice":[54],"that":[55,137,165,192,203],"reflects":[56],"realistic":[58],"deployment":[59],"scenario":[60],"where":[61],"practitioners":[62],"lack":[63],"labeled":[64],"training":[66],"data.":[67],"Our":[68],"central":[69],"finding":[70],"is":[71,91,130,141,172],"pervasive":[73],"calibration":[74,146],"failure":[75,95],"invisible":[76],"under":[77],"single-threshold":[78],"protocols:":[79],"achieve":[81],"moderate":[82],"Pixel-AUC":[83],"(&gt;=0.76)":[84],"yet":[85],"near-zero":[86],"Pixel-F1.":[87],"This":[88],"AUC-F1":[89],"gap":[90,225],"not":[92,139,169],"discrimination":[94],"but":[96],"score-distribution":[98],"shift:":[99],"tampered":[100],"regions":[101],"occupy":[102],"only":[103],"0.27-4.17%":[104],"of":[105,113,160,210],"pixels":[106],"in":[107,117],"images":[109,157],"an":[111,197],"order":[112],"magnitude":[114],"less":[115],"than":[116,133],"natural":[118],"image":[119],"benchmarks":[120,214],"making":[122],"standard":[124],"tau=0.5":[125],"threshold":[126,153,166],"catastrophically":[127],"miscalibrated.":[128],"Oracle-F1":[129,162],"2-10x":[131],"higher":[132],"fixed-threshold":[134],"Pixel-F1,":[135],"confirming":[136],"calibration,":[138],"representation,":[140],"bottleneck.":[143],"A":[144],"controlled":[145],"experiment":[147],"validates":[148],"this:":[149],"adapting":[150],"single":[152],"on":[154,187,226],"N=10":[155],"recovers":[158],"39-55%":[159],"gap,":[163],"demonstrating":[164],"retraining":[170],"key":[174],"missing":[175],"step":[176],"practical":[178],"deployment.":[179],"Overall,":[180],"evaluated":[182],"method":[183],"works":[184],"reliably":[185],"out-of-the-box":[186],"diverse":[188],"types,":[190],"underscoring":[191],"detection":[195],"remains":[196],"unsolved":[198],"problem.":[199],"further":[201],"note":[202],"predate":[207],"era":[209],"generative":[211],"AI":[212],"editing;":[213],"covering":[215],"diffusion-":[216],"LLM-based":[218],"forgeries":[220],"represent":[221],"critical":[223],"open":[224],"modern":[228],"attack":[229],"surface.":[230]},"counts_by_year":[],"updated_date":"2026-03-12T06:13:28.667946","created_date":"2026-03-04T00:00:00"}
