{"id":"https://openalex.org/W7163421469","doi":"https://doi.org/10.48550/arxiv.2606.02946","title":"Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessment","display_name":"Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessment","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163421469","doi":"https://doi.org/10.48550/arxiv.2606.02946"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.02946","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02946","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.02946","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137781455","display_name":"Yiran Qiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiao, Yiran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137735702","display_name":"Jing Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137806253","display_name":"Jiaqi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137782005","display_name":"Yang Liu (4829)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005667980","display_name":"Qiwei Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Qiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137780491","display_name":"Xiang Ao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ao, Xiang","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.47850000858306885,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.47850000858306885,"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/T11644","display_name":"Spam and Phishing Detection","score":0.09570000320672989,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11147","display_name":"Misinformation and Its Impacts","score":0.0348999984562397,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.8355000019073486},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8352000117301941},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5097000002861023},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.47040000557899475},{"id":"https://openalex.org/keywords/security-domain","display_name":"Security domain","score":0.4341999888420105},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.43130001425743103}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.8355000019073486},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8352000117301941},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6877999901771545},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5097000002861023},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.47040000557899475},{"id":"https://openalex.org/C2780264999","wikidata":"https://www.wikidata.org/wiki/Q7445032","display_name":"Security domain","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.40700000524520874},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3246000111103058},{"id":"https://openalex.org/C98184364","wikidata":"https://www.wikidata.org/wiki/Q1780131","display_name":"Argument (complex analysis)","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29829999804496765},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26350000500679016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26089999079704285}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.02946","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02946","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.02946","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02946","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Reduced inequalities","score":0.4157305657863617,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Live":[0],"streaming":[1,100],"has":[2],"emerged":[3],"as":[4],"a":[5,85,94,140],"primary":[6],"medium":[7],"for":[8,97],"social":[9],"interaction":[10],"and":[11,73,89,114,121,156],"digital":[12],"commerce,":[13],"yet":[14],"it":[15],"is":[16,28,182],"increasingly":[17],"plagued":[18],"by":[19,111],"sophisticated":[20],"risks.":[21],"A":[22],"fundamental":[23],"challenge":[24],"in":[25,65,170,175],"this":[26,78,82],"domain":[27],"\\emph{tactical":[29],"out-of-distribution":[30],"(OOD)":[31],"shift}:":[32],"while":[33],"malicious":[34,133],"actors":[35],"maintain":[36],"stable":[37,132],"underlying":[38],"objectives,":[39],"they":[40],"continuously":[41],"redesign":[42],"narrative":[43,115],"packaging":[44],"to":[45,63,126,144],"evade":[46],"detection.":[47],"Such":[48],"adversarial":[49,108,173],"shifts":[50],"expose":[51],"critical":[52],"limitations":[53],"of":[54,68],"existing":[55],"OOD":[56],"generalization":[57],"paradigms,":[58],"whose":[59],"assumptions":[60],"are":[61],"difficult":[62],"satisfy":[64],"the":[66,118],"presence":[67],"tightly":[69],"coupled":[70],"intent-tactic":[71],"evolution":[72],"ill-defined":[74],"raw-level":[75],"counterfactuals.":[76],"In":[77],"paper,":[79],"we":[80],"tackle":[81],"issue":[83],"from":[84],"\\emph{latent":[86,123],"causal}":[87],"perspective":[88],"propose":[90],"\\underline{L}atent-\\underline{P}redictive":[91],"\\underline{C}ounterfactual":[92],"\\underline{D}ecoupling~(LPCD),":[93],"plug-in":[95],"framework":[96],"robust":[98],"live":[99,177],"risk":[101,128],"assessment.":[102],"LPCD":[103,138,162],"enables":[104],"counterfactual":[105,124],"reasoning":[106],"under":[107],"tactical":[109],"re-packaging":[110],"modeling":[112],"intent":[113],"variation":[116],"at":[117,184],"latent":[119],"level,":[120],"enforces":[122],"consistency}":[125],"anchor":[127],"prediction":[129],"on":[130,152],"causally":[131],"intent.":[134],"At":[135],"inference":[136],"time,":[137],"applies":[139],"lightweight,":[141],"parameter-free":[142],"calibration":[143],"further":[145],"mitigate":[146],"tactic-induced":[147],"distribution":[148],"shifts.":[149],"Extensive":[150],"experiments":[151],"large-scale":[153],"industrial":[154],"datasets":[155],"online":[157],"production":[158],"traffic":[159],"demonstrate":[160],"that":[161],"consistently":[163],"outperforms":[164],"state-of-the-art":[165],"baselines,":[166],"validating":[167],"its":[168],"effectiveness":[169],"moderating":[171],"evolving":[172],"risks":[174],"real-world":[176],"streaming.":[178],"The":[179],"project":[180],"page":[181],"available":[183],"https://qiaoyran.github.io/LiveStreamingRiskAssessment/.":[185]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-04T00:00:00"}
