{"id":"https://openalex.org/W7138133984","doi":"https://doi.org/10.48550/arxiv.2603.14304","title":"A Physically-Grounded Attack and Adaptive Defense Framework for Real-World Low-Light Image Enhancement","display_name":"A Physically-Grounded Attack and Adaptive Defense Framework for Real-World Low-Light Image Enhancement","publication_year":2026,"publication_date":"2026-03-15","ids":{"openalex":"https://openalex.org/W7138133984","doi":"https://doi.org/10.48550/arxiv.2603.14304"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14304","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14304","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.2603.14304","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004238321","display_name":"Tongshun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Tongshun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129671358","display_name":"Pingping Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Pingping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054326846","display_name":"Yuqing Lei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lei, Yuqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128687155","display_name":"Zixuan Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Zixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032424116","display_name":"Qiuzhan Zhou","orcid":"https://orcid.org/0000-0002-9486-7144"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Qiuzhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129732102","display_name":"Zhiyuan Zha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zha, Zhiyuan","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/T11019","display_name":"Image Enhancement Techniques","score":0.9190000295639038,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9190000295639038,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.0414000004529953,"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":0.010200000368058681,"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/noise","display_name":"Noise (video)","score":0.6140999794006348},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4291999936103821},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42340001463890076},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.41429999470710754},{"id":"https://openalex.org/keywords/artificial-noise","display_name":"Artificial noise","score":0.4020000100135803},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3971000015735626},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.39489999413490295},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.39329999685287476},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.38999998569488525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6909999847412109},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6140999794006348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49050000309944153},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4291999936103821},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4253999888896942},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42340001463890076},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C2780909371","wikidata":"https://www.wikidata.org/wiki/Q4801092","display_name":"Artificial noise","level":4,"score":0.4020000100135803},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3971000015735626},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.39489999413490295},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.38999998569488525},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.37790000438690186},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3691999912261963},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.3603000044822693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32269999384880066},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28540000319480896},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.27480000257492065},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25600001215934753},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2547000050544739},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14304","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14304","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.2603.14304","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14304","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Limited":[0],"illumination":[1],"often":[2],"causes":[3],"severe":[4,185],"physical":[5,30],"noise":[6,31,130,159,178,206],"and":[7,54,91,94],"detail":[8],"degradation":[9,110,133],"in":[10,156],"images.":[11],"Existing":[12],"Low-Light":[13],"Image":[14,78],"Enhancement":[15],"(LLIE)":[16],"methods":[17],"frequently":[18],"treat":[19],"the":[20,29,60,84,96,99,120,136,172],"enhancement":[21,197],"process":[22],"as":[23,50],"a":[24,44,51,65,125,143],"blind":[25],"black-box":[26],"mapping,":[27],"overlooking":[28],"transformation":[32],"during":[33],"imaging,":[34],"leading":[35],"to":[36,83,98,153],"suboptimal":[37],"performance.":[38],"To":[39],"address":[40],"this,":[41],"we":[42,63,123,162],"propose":[43],"novel":[45],"LLIE":[46,201],"approach,":[47],"conceptually":[48],"formulated":[49],"physics-based":[52,66],"attack":[53,61],"display-adaptive":[55],"defense":[56,121],"paradigm.":[57],"Specifically,":[58],"on":[59,116,177],"side,":[62,122],"establish":[64],"Degradation":[67],"Synthesis":[68],"(PDS)":[69],"pipeline.":[70],"Unlike":[71],"standard":[72],"data":[73,97],"augmentation,":[74],"PDS":[75],"explicitly":[76,108],"models":[77],"Signal":[79],"Processor":[80],"(ISP)":[81],"inversion":[82],"RAW":[85],"domain,":[86],"injects":[87],"physically":[88],"plausible":[89],"photon":[90],"read":[92],"noise,":[93],"re-projects":[95],"sRGB":[100,138],"domain.":[101],"This":[102],"generates":[103],"high-fidelity":[104],"training":[105],"pairs":[106],"with":[107],"parameterized":[109],"vectors,":[111],"effectively":[112,203],"simulating":[113],"realistic":[114],"attacks":[115],"clean":[117],"signals.":[118],"On":[119],"construct":[124],"dual-layer":[126],"fortified":[127],"system.":[128],"A":[129],"predictor":[131],"estimates":[132,141],"parameters":[134],"from":[135],"input":[137],"image.":[139],"These":[140],"guide":[142],"degradation-aware":[144],"Mixture":[145],"of":[146],"Experts":[147],"(DA-MoE),":[148],"which":[149],"dynamically":[150,170],"routes":[151],"features":[152],"experts":[154],"specialized":[155],"handling":[157],"specific":[158],"intensities.":[160],"Furthermore,":[161],"introduce":[163],"an":[164],"Adaptive":[165],"Metric":[166],"Defense":[167],"(AMD)":[168],"mechanism,":[169],"calibrating":[171],"feature":[173],"embedding":[174],"space":[175],"based":[176],"severity,":[179],"ensuring":[180],"robust":[181],"representation":[182],"learning":[183],"under":[184],"degradation.":[186],"Extensive":[187],"experiments":[188],"demonstrate":[189],"that":[190],"our":[191],"approach":[192],"offers":[193],"significant":[194],"plug-and-play":[195],"performance":[196],"for":[198],"existing":[199],"benchmark":[200],"methods,":[202],"suppressing":[204],"real-world":[205],"while":[207],"preserving":[208],"structural":[209],"fidelity.":[210],"The":[211],"sourced":[212],"code":[213],"is":[214],"available":[215],"at":[216],"https://github.com/bywlzts/Attack-defense-llie.":[217]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}