{"id":"https://openalex.org/W7165625188","doi":"https://doi.org/10.48550/arxiv.2606.22988","title":"Public Diffusion Models, Private Images: Key-Controlled Inversion for Conditional Reconstruction","display_name":"Public Diffusion Models, Private Images: Key-Controlled Inversion for Conditional Reconstruction","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165625188","doi":"https://doi.org/10.48550/arxiv.2606.22988"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.22988","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22988","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.2606.22988","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126719030","display_name":"Lijunxian Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lijunxian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139185876","display_name":"Weihai Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Weihai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139211164","display_name":"Bin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073136550","display_name":"Zhewei Xu","orcid":"https://orcid.org/0000-0003-4718-6175"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Zikai","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/T10237","display_name":"Cryptography and Data Security","score":0.349700003862381,"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"}},"topics":[{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.349700003862381,"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"}},{"id":"https://openalex.org/T11017","display_name":"Chaos-based Image/Signal Encryption","score":0.12680000066757202,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.1151999980211258,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.5843999981880188},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.553600013256073},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4925000071525574},{"id":"https://openalex.org/keywords/ciphertext","display_name":"Ciphertext","score":0.41780000925064087},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.38920000195503235},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.382099986076355},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.36739999055862427},{"id":"https://openalex.org/keywords/exponential-growth","display_name":"Exponential growth","score":0.36579999327659607}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6733999848365784},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5843999981880188},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.553600013256073},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4521999955177307},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44609999656677246},{"id":"https://openalex.org/C93974786","wikidata":"https://www.wikidata.org/wiki/Q1589480","display_name":"Ciphertext","level":3,"score":0.41780000925064087},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.382099986076355},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C75235859","wikidata":"https://www.wikidata.org/wiki/Q582659","display_name":"Exponential growth","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3538999855518341},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.34769999980926514},{"id":"https://openalex.org/C68710425","wikidata":"https://www.wikidata.org/wiki/Q5275442","display_name":"Diffusion process","level":3,"score":0.3427000045776367},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.33410000801086426},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C55350006","wikidata":"https://www.wikidata.org/wiki/Q237193","display_name":"Exponential distribution","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3140000104904175},{"id":"https://openalex.org/C147343967","wikidata":"https://www.wikidata.org/wiki/Q5159078","display_name":"Concrete security","level":3,"score":0.3100999891757965},{"id":"https://openalex.org/C163173736","wikidata":"https://www.wikidata.org/wiki/Q3308558","display_name":"Key generation","level":3,"score":0.29420000314712524},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.26429998874664307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.22988","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22988","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.2606.22988","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.22988","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":[{"display_name":"Peace, Justice and strong institutions","score":0.6373753547668457,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"models":[1,53,186],"are":[2,10,60,82],"often":[3],"deployed":[4],"in":[5,165],"settings":[6],"where":[7,79],"model":[8,57,207],"parameters":[9,59,81],"publicly":[11],"accessible":[12],"(e.g.,":[13],"open-source":[14],"libraries":[15],"or":[16],"released":[17],"checkpoints).":[18],"This":[19],"white-box":[20,77],"scenario":[21],"creates":[22],"a":[23,55,86,105,120,166],"serious":[24],"security":[25,106,168,182],"risk:":[26],"any":[27,131,173],"user":[28,121],"who":[29],"obtains":[30],"an":[31,65],"intermediate":[32],"latent":[33],"representation":[34],"can":[35,126],"invert":[36],"the":[37,41,72,92,113,123,128,149,159,195,202],"process":[38],"to":[39],"recover":[40],"original":[42,129],"input":[43],"image.":[44],"Most":[45],"prior":[46],"work":[47],"on":[48],"access":[49],"control":[50],"for":[51,143,172],"generative":[52],"assumes":[54],"black-box":[56],"(i.e.,":[58],"kept":[61],"secret),":[62],"typically":[63],"under":[64],"honest-but-curious":[66],"adversary.":[67,177],"By":[68,108],"contrast,":[69],"we":[70,116,146,179],"address":[71],"more":[73],"challenging":[74],"and":[75,156,187,189],"realistic":[76],"setting":[78],"all":[80],"public.":[83],"We":[84],"present":[85],"key-controlled":[87],"inversion":[88,114],"framework":[89],"that":[90,118,148,158,194],"turns":[91],"inherent":[93],"error":[94],"propagation":[95],"of":[96],"diffusion":[97,144],"models,":[98,145],"which":[99],"exponentially":[100,163],"amplifies":[101],"small":[102,164],"perturbations,":[103],"into":[104,112],"asset.":[107],"injecting":[109],"key-dependent":[110],"noise":[111,198],"formula,":[115],"ensure":[117],"only":[119],"with":[122],"correct":[124],"key":[125,133,197],"reconstruct":[127],"image;":[130],"other":[132],"yields":[134],"unrecognizable":[135],"output.":[136],"Theoretically,":[137],"by":[138,206],"leveraging":[139],"existing":[140],"error-propagation":[141],"theory":[142],"prove":[147],"resulting":[150],"ciphertext":[151],"distribution":[152],"is":[153,162],"IND-CPA":[154],"secure":[155],"derive":[157],"adversary's":[160],"advantage":[161],"tunable":[167],"parameter,":[169],"hence":[170],"negligible":[171],"probabilistic":[174],"polynomial-time":[175],"(PPT)":[176],"Experimentally,":[178],"validate":[180],"these":[181],"guarantees":[183],"across":[184],"several":[185],"datasets":[188],"further":[190],"demonstrate":[191],"cross-model":[192],"robustness,":[193],"injected":[196],"does":[199],"not":[200],"amplify":[201],"performance":[203],"drop":[204],"caused":[205],"discrepancies.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}