{"id":"https://openalex.org/W3135322301","doi":"https://doi.org/10.1109/wifs49906.2020.9360893","title":"Reliable JPEG Forensics via Model Uncertainty","display_name":"Reliable JPEG Forensics via Model Uncertainty","publication_year":2020,"publication_date":"2020-12-06","ids":{"openalex":"https://openalex.org/W3135322301","doi":"https://doi.org/10.1109/wifs49906.2020.9360893","mag":"3135322301"},"language":"en","primary_location":{"id":"doi:10.1109/wifs49906.2020.9360893","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wifs49906.2020.9360893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Workshop on Information Forensics and Security (WIFS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008546345","display_name":"Benedikt Lorch","orcid":"https://orcid.org/0000-0002-7843-4656"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Benedikt Lorch","raw_affiliation_strings":["IT Security Infrastructure Lab, Friedrich-Alexander University Erlangen-N\u00fcrnberg"],"affiliations":[{"raw_affiliation_string":"IT Security Infrastructure Lab, Friedrich-Alexander University Erlangen-N\u00fcrnberg","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073442830","display_name":"Anatol Maier","orcid":"https://orcid.org/0000-0002-8093-7252"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Anatol Maier","raw_affiliation_strings":["IT Security Infrastructure Lab, Friedrich-Alexander University Erlangen-N\u00fcrnberg"],"affiliations":[{"raw_affiliation_string":"IT Security Infrastructure Lab, Friedrich-Alexander University Erlangen-N\u00fcrnberg","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049250339","display_name":"Christian Rie\u00df","orcid":"https://orcid.org/0000-0002-5556-5338"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Riess","raw_affiliation_strings":["IT Security Infrastructure Lab, Friedrich-Alexander University Erlangen-N\u00fcrnberg"],"affiliations":[{"raw_affiliation_string":"IT Security Infrastructure Lab, Friedrich-Alexander University Erlangen-N\u00fcrnberg","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5008546345"],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":null,"apc_paid":null,"fwci":0.7816,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75179808,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12357","display_name":"Digital Media Forensic Detection","score":1.0,"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":1.0,"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":0.9965999722480774,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9909999966621399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7737570405006409},{"id":"https://openalex.org/keywords/jpeg","display_name":"JPEG","score":0.6927508115768433},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6355772614479065},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5898220539093018},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5554025173187256},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5187711715698242},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.4973964989185333},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.48898637294769287},{"id":"https://openalex.org/keywords/jpeg-2000","display_name":"JPEG 2000","score":0.4714157283306122},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4560537040233612},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4316653907299042},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4241664707660675},{"id":"https://openalex.org/keywords/lossless-jpeg","display_name":"Lossless JPEG","score":0.41144615411758423},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4067639410495758},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.3884805142879486},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38271716237068176},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3403967618942261},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.22954177856445312},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.17304924130439758},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1478482484817505},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10326716303825378},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.07906332612037659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7737570405006409},{"id":"https://openalex.org/C198751489","wikidata":"https://www.wikidata.org/wiki/Q2195","display_name":"JPEG","level":3,"score":0.6927508115768433},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6355772614479065},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5898220539093018},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5554025173187256},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5187711715698242},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.4973964989185333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48898637294769287},{"id":"https://openalex.org/C69216139","wikidata":"https://www.wikidata.org/wiki/Q931783","display_name":"JPEG 2000","level":5,"score":0.4714157283306122},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4560537040233612},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4316653907299042},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4241664707660675},{"id":"https://openalex.org/C8384606","wikidata":"https://www.wikidata.org/wiki/Q2190356","display_name":"Lossless JPEG","level":5,"score":0.41144615411758423},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4067639410495758},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3884805142879486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38271716237068176},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3403967618942261},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.22954177856445312},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.17304924130439758},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1478482484817505},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10326716303825378},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.07906332612037659},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wifs49906.2020.9360893","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wifs49906.2020.9360893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Workshop on Information Forensics and Security (WIFS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.49000000953674316,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1496451467","https://openalex.org/W1587416387","https://openalex.org/W1967503138","https://openalex.org/W2050991548","https://openalex.org/W2055745001","https://openalex.org/W2070040249","https://openalex.org/W2111280862","https://openalex.org/W2142694135","https://openalex.org/W2154708688","https://openalex.org/W2164411961","https://openalex.org/W2548302937","https://openalex.org/W2553152707","https://openalex.org/W2574852830","https://openalex.org/W2600383743","https://openalex.org/W2806156052","https://openalex.org/W2888338418","https://openalex.org/W2888822137","https://openalex.org/W2890588722","https://openalex.org/W2900420778","https://openalex.org/W2959930619","https://openalex.org/W2963238274","https://openalex.org/W2964059111","https://openalex.org/W2970859221","https://openalex.org/W2991005702","https://openalex.org/W3089377254","https://openalex.org/W3148833453","https://openalex.org/W4212863985","https://openalex.org/W6617145748","https://openalex.org/W6641712419","https://openalex.org/W6676669043","https://openalex.org/W6682994687","https://openalex.org/W6684488266","https://openalex.org/W6730042731"],"related_works":["https://openalex.org/W3190486427","https://openalex.org/W4243959093","https://openalex.org/W1998280942","https://openalex.org/W1992410481","https://openalex.org/W2441603424","https://openalex.org/W1980149576","https://openalex.org/W2145138541","https://openalex.org/W2026465795","https://openalex.org/W3137515556","https://openalex.org/W4385625475"],"abstract_inverted_index":{"Many":[0],"methods":[1],"in":[2,69,89,124,199,204],"image":[3],"forensics":[4],"are":[5],"sensitive":[6],"to":[7,21,28,33,42,86,131],"varying":[8],"amounts":[9],"of":[10,46,115,119,145,154],"JPEG":[11,30,47,156],"compression.":[12,158],"To":[13,74],"mitigate":[14],"this":[15,76],"issue,":[16],"it":[17,174],"is":[18,40,53,84,102,149,181],"either":[19],"possible":[20],"a)":[22],"build":[23],"detectors":[24],"that":[25,83,190],"better":[26],"generalize":[27],"unknown":[29,140],"settings,":[31],"or":[32],"b)":[34],"train":[35],"multiple":[36],"detectors,":[37],"where":[38],"each":[39],"specialized":[41],"a":[43,80,196],"narrow":[44],"range":[45],"qualities.":[48],"While":[49],"the":[50,58,99,132,146,152,178,185,191],"first":[51],"approach":[52,60],"currently":[54],"an":[55,113,116],"open":[56],"challenge,":[57,77],"second":[59],"may":[61],"silently":[62],"fail,":[63],"even":[64],"for":[65,97,139,208],"only":[66],"slight":[67],"mismatches":[68],"training":[70,100,133,186],"and":[71,173,203],"testing":[72],"distributions.":[73],"alleviate":[75],"we":[78,107],"propose":[79,108],"forensic":[81,197],"detector":[82,160,201],"able":[85],"express":[87],"uncertainty":[88],"its":[90,125,136],"predictions.":[91],"This":[92],"allows":[93],"detecting":[94,155],"test":[95,128,141,179],"samples":[96,129],"which":[98],"distribution":[101],"not":[103,182],"representative.":[104],"More":[105],"specifically,":[106],"Bayesian":[109],"logistic":[110],"regression":[111],"as":[112],"instance":[114],"infinite":[117],"ensemble":[118,122],"classifiers.":[120],"The":[121,143,159],"agrees":[123],"predictions":[126,137],"from":[127],"similar":[130],"data":[134,180],"but":[135],"diverge":[138],"samples.":[142],"applicability":[144],"proposed":[147,192],"method":[148,193],"evaluated":[150],"on":[151,164],"task":[153],"double":[157],"achieves":[161],"high":[162],"performance":[163],"two":[165],"goals":[166],"simultaneously:":[167],"It":[168],"accurately":[169,175],"detects":[170],"double-JPEG":[171],"compression,":[172],"indicates":[176],"when":[177],"covered":[183],"by":[184],"data.":[187],"We":[188],"assert":[189],"can":[194],"assist":[195],"analyst":[198],"assessing":[200],"reliability":[202],"anticipating":[205],"failure":[206],"cases":[207],"specific":[209],"inputs.":[210]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}