{"id":"https://openalex.org/W7163132327","doi":"https://doi.org/10.48550/arxiv.2606.02178","title":"Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization","display_name":"Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163132327","doi":"https://doi.org/10.48550/arxiv.2606.02178"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.02178","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02178","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.2606.02178","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137699175","display_name":"Yiming Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yiming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137710323","display_name":"Baiqi Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Baiqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137644155","display_name":"Qingming Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qingming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137652371","display_name":"Jiahao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiahao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137637805","display_name":"Tong Zhang","orcid":"https://orcid.org/0000-0002-0557-7090"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137668820","display_name":"Shouling Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji, Shouling","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":0.9169999957084656,"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":0.9169999957084656,"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":0.039900001138448715,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.015200000256299973,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.635699987411499},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5756000280380249},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5235999822616577},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5231000185012817},{"id":"https://openalex.org/keywords/image-editing","display_name":"Image editing","score":0.4575999975204468},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.448199987411499},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4429999887943268},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4381999969482422},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.42649999260902405}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6858999729156494},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6460999846458435},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.635699987411499},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5756000280380249},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5235999822616577},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49939998984336853},{"id":"https://openalex.org/C2776674983","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image editing","level":3,"score":0.4575999975204468},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4429999887943268},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4381999969482422},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.3391999900341034},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.31679999828338623},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2603999972343445},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.02178","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02178","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.2606.02178","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02178","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":[{"score":0.4460160732269287,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,31],"generative":[3,108,144],"AI":[4],"have":[5],"led":[6],"to":[7,80,92,99,142],"image":[8,19],"editing":[9],"models":[10],"capable":[11],"of":[12,62],"producing":[13],"realistic":[14],"forgeries":[15],"that":[16,41,55,75,125],"evade":[17],"traditional":[18],"forgery":[20,96,137],"localization":[21],"methods,":[22],"as":[23],"these":[24,82],"approaches":[25],"depend":[26],"on":[27,135],"physical":[28],"noise":[29],"absent":[30],"synthetic":[32],"data.":[33],"To":[34],"address":[35],"this":[36,67],"challenge,":[37],"we":[38,69,110],"theoretically":[39],"demonstrate":[40,124],"the":[42,59,101],"diffusion":[43],"process":[44],"inherently":[45],"suppresses":[46],"local":[47],"high-frequency":[48],"variance,":[49],"creating":[50],"a":[51,72,77,87,128],"statistical":[52],"energy":[53],"gap":[54],"is":[56,148],"distinguishable":[57],"from":[58],"natural":[60],"entropy":[61],"optical":[63],"imaging.":[64],"Guided":[65],"by":[66],"insight,":[68],"propose":[70],"FLAME,":[71],"unified":[73],"framework":[74],"utilizes":[76],"LAD":[78],"map":[79],"capture":[81],"intrinsic":[83],"anomalies,":[84],"coupled":[85],"with":[86],"parameter-efficient":[88],"adapter":[89],"for":[90,116],"SAM":[91],"achieve":[93],"precise,":[94],"pixel-level":[95],"localization.":[97],"Furthermore,":[98],"bridge":[100],"lag":[102],"between":[103],"forensic":[104],"benchmarks":[105],"and":[106],"evolving":[107],"models,":[109],"introduce":[111],"EditStream,":[112],"an":[113],"automated":[114],"pipeline":[115],"continuous,":[117],"instruction-based":[118],"training":[119],"data":[120],"synthesis.":[121],"Extensive":[122],"experiments":[123],"FLAME":[126],"establishes":[127],"new":[129],"state-of-the-art,":[130],"significantly":[131],"outperforming":[132],"previous":[133],"methods":[134],"AI-generated":[136],"datasets":[138],"while":[139],"effectively":[140],"generalizing":[141],"unseen":[143],"architectures.":[145],"Our":[146],"code":[147],"available":[149],"at":[150],"https://github.com/phoenixnir/FLAME.":[151]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}